#importing all the necessary libraries

import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn import svm
from sklearn.svm import SVC
from sklearn.metrics import mean_absolute_percentage_error
from sklearn.preprocessing import RobustScaler, MinMaxScaler
import sweetviz as sv
import warnings
from sklearn.feature_selection import VarianceThreshold
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import RobustScaler, MinMaxScaler
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Ridge
from sklearn.linear_model import Lasso
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from sklearn.neighbors import KNeighborsRegressor
from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import GradientBoostingRegressor

df = pd.read_csv(r"C:\Users\HP\Downloads\train.csv")
print("Full train dataset shape is {}".format(df.shape))
df_org = df.copy()

Full train dataset shape is (1460, 81)

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1460 entries, 0 to 1459
Data columns (total 81 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Id             1460 non-null   int64  
 1   MSSubClass     1460 non-null   int64  
 2   MSZoning       1460 non-null   object 
 3   LotFrontage    1201 non-null   float64
 4   LotArea        1460 non-null   int64  
 5   Street         1460 non-null   object 
 6   Alley          91 non-null     object 
 7   LotShape       1460 non-null   object 
 8   LandContour    1460 non-null   object 
 9   Utilities      1460 non-null   object 
 10  LotConfig      1460 non-null   object 
 11  LandSlope      1460 non-null   object 
 12  Neighborhood   1460 non-null   object 
 13  Condition1     1460 non-null   object 
 14  Condition2     1460 non-null   object 
 15  BldgType       1460 non-null   object 
 16  HouseStyle     1460 non-null   object 
 17  OverallQual    1460 non-null   int64  
 18  OverallCond    1460 non-null   int64  
 19  YearBuilt      1460 non-null   int64  
 20  YearRemodAdd   1460 non-null   int64  
 21  RoofStyle      1460 non-null   object 
 22  RoofMatl       1460 non-null   object 
 23  Exterior1st    1460 non-null   object 
 24  Exterior2nd    1460 non-null   object 
 25  MasVnrType     1452 non-null   object 
 26  MasVnrArea     1452 non-null   float64
 27  ExterQual      1460 non-null   object 
 28  ExterCond      1460 non-null   object 
 29  Foundation     1460 non-null   object 
 30  BsmtQual       1423 non-null   object 
 31  BsmtCond       1423 non-null   object 
 32  BsmtExposure   1422 non-null   object 
 33  BsmtFinType1   1423 non-null   object 
 34  BsmtFinSF1     1460 non-null   int64  
 35  BsmtFinType2   1422 non-null   object 
 36  BsmtFinSF2     1460 non-null   int64  
 37  BsmtUnfSF      1460 non-null   int64  
 38  TotalBsmtSF    1460 non-null   int64  
 39  Heating        1460 non-null   object 
 40  HeatingQC      1460 non-null   object 
 41  CentralAir     1460 non-null   object 
 42  Electrical     1459 non-null   object 
 43  1stFlrSF       1460 non-null   int64  
 44  2ndFlrSF       1460 non-null   int64  
 45  LowQualFinSF   1460 non-null   int64  
 46  GrLivArea      1460 non-null   int64  
 47  BsmtFullBath   1460 non-null   int64  
 48  BsmtHalfBath   1460 non-null   int64  
 49  FullBath       1460 non-null   int64  
 50  HalfBath       1460 non-null   int64  
 51  BedroomAbvGr   1460 non-null   int64  
 52  KitchenAbvGr   1460 non-null   int64  
 53  KitchenQual    1460 non-null   object 
 54  TotRmsAbvGrd   1460 non-null   int64  
 55  Functional     1460 non-null   object 
 56  Fireplaces     1460 non-null   int64  
 57  FireplaceQu    770 non-null    object 
 58  GarageType     1379 non-null   object 
 59  GarageYrBlt    1379 non-null   float64
 60  GarageFinish   1379 non-null   object 
 61  GarageCars     1460 non-null   int64  
 62  GarageArea     1460 non-null   int64  
 63  GarageQual     1379 non-null   object 
 64  GarageCond     1379 non-null   object 
 65  PavedDrive     1460 non-null   object 
 66  WoodDeckSF     1460 non-null   int64  
 67  OpenPorchSF    1460 non-null   int64  
 68  EnclosedPorch  1460 non-null   int64  
 69  3SsnPorch      1460 non-null   int64  
 70  ScreenPorch    1460 non-null   int64  
 71  PoolArea       1460 non-null   int64  
 72  PoolQC         7 non-null      object 
 73  Fence          281 non-null    object 
 74  MiscFeature    54 non-null     object 
 75  MiscVal        1460 non-null   int64  
 76  MoSold         1460 non-null   int64  
 77  YrSold         1460 non-null   int64  
 78  SaleType       1460 non-null   object 
 79  SaleCondition  1460 non-null   object 
 80  SalePrice      1460 non-null   int64  
dtypes: float64(3), int64(35), object(43)
memory usage: 924.0+ KB

df.isnull()

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	...	PoolArea	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition	SalePrice
0	False	False	False	False	False	False	True	False	False	False	...	False	True	True	True	False	False	False	False	False	False
1	False	False	False	False	False	False	True	False	False	False	...	False	True	True	True	False	False	False	False	False	False
2	False	False	False	False	False	False	True	False	False	False	...	False	True	True	True	False	False	False	False	False	False
3	False	False	False	False	False	False	True	False	False	False	...	False	True	True	True	False	False	False	False	False	False
4	False	False	False	False	False	False	True	False	False	False	...	False	True	True	True	False	False	False	False	False	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1455	False	False	False	False	False	False	True	False	False	False	...	False	True	True	True	False	False	False	False	False	False
1456	False	False	False	False	False	False	True	False	False	False	...	False	True	False	True	False	False	False	False	False	False
1457	False	False	False	False	False	False	True	False	False	False	...	False	True	False	False	False	False	False	False	False	False
1458	False	False	False	False	False	False	True	False	False	False	...	False	True	True	True	False	False	False	False	False	False
1459	False	False	False	False	False	False	True	False	False	False	...	False	True	True	True	False	False	False	False	False	False

1460 rows × 81 columns

# Checking descriptive statistics
df.describe().T

	count	mean	std	min	25%	50%	75%	max
Id	1460.0	730.500000	421.610009	1.0	365.75	730.5	1095.25	1460.0
MSSubClass	1460.0	56.897260	42.300571	20.0	20.00	50.0	70.00	190.0
LotFrontage	1201.0	70.049958	24.284752	21.0	59.00	69.0	80.00	313.0
LotArea	1460.0	10516.828082	9981.264932	1300.0	7553.50	9478.5	11601.50	215245.0
OverallQual	1460.0	6.099315	1.382997	1.0	5.00	6.0	7.00	10.0
OverallCond	1460.0	5.575342	1.112799	1.0	5.00	5.0	6.00	9.0
YearBuilt	1460.0	1971.267808	30.202904	1872.0	1954.00	1973.0	2000.00	2010.0
YearRemodAdd	1460.0	1984.865753	20.645407	1950.0	1967.00	1994.0	2004.00	2010.0
MasVnrArea	1452.0	103.685262	181.066207	0.0	0.00	0.0	166.00	1600.0
BsmtFinSF1	1460.0	443.639726	456.098091	0.0	0.00	383.5	712.25	5644.0
BsmtFinSF2	1460.0	46.549315	161.319273	0.0	0.00	0.0	0.00	1474.0
BsmtUnfSF	1460.0	567.240411	441.866955	0.0	223.00	477.5	808.00	2336.0
TotalBsmtSF	1460.0	1057.429452	438.705324	0.0	795.75	991.5	1298.25	6110.0
1stFlrSF	1460.0	1162.626712	386.587738	334.0	882.00	1087.0	1391.25	4692.0
2ndFlrSF	1460.0	346.992466	436.528436	0.0	0.00	0.0	728.00	2065.0
LowQualFinSF	1460.0	5.844521	48.623081	0.0	0.00	0.0	0.00	572.0
GrLivArea	1460.0	1515.463699	525.480383	334.0	1129.50	1464.0	1776.75	5642.0
BsmtFullBath	1460.0	0.425342	0.518911	0.0	0.00	0.0	1.00	3.0
BsmtHalfBath	1460.0	0.057534	0.238753	0.0	0.00	0.0	0.00	2.0
FullBath	1460.0	1.565068	0.550916	0.0	1.00	2.0	2.00	3.0
HalfBath	1460.0	0.382877	0.502885	0.0	0.00	0.0	1.00	2.0
BedroomAbvGr	1460.0	2.866438	0.815778	0.0	2.00	3.0	3.00	8.0
KitchenAbvGr	1460.0	1.046575	0.220338	0.0	1.00	1.0	1.00	3.0
TotRmsAbvGrd	1460.0	6.517808	1.625393	2.0	5.00	6.0	7.00	14.0
Fireplaces	1460.0	0.613014	0.644666	0.0	0.00	1.0	1.00	3.0
GarageYrBlt	1379.0	1978.506164	24.689725	1900.0	1961.00	1980.0	2002.00	2010.0
GarageCars	1460.0	1.767123	0.747315	0.0	1.00	2.0	2.00	4.0
GarageArea	1460.0	472.980137	213.804841	0.0	334.50	480.0	576.00	1418.0
WoodDeckSF	1460.0	94.244521	125.338794	0.0	0.00	0.0	168.00	857.0
OpenPorchSF	1460.0	46.660274	66.256028	0.0	0.00	25.0	68.00	547.0
EnclosedPorch	1460.0	21.954110	61.119149	0.0	0.00	0.0	0.00	552.0
3SsnPorch	1460.0	3.409589	29.317331	0.0	0.00	0.0	0.00	508.0
ScreenPorch	1460.0	15.060959	55.757415	0.0	0.00	0.0	0.00	480.0
PoolArea	1460.0	2.758904	40.177307	0.0	0.00	0.0	0.00	738.0
MiscVal	1460.0	43.489041	496.123024	0.0	0.00	0.0	0.00	15500.0
MoSold	1460.0	6.321918	2.703626	1.0	5.00	6.0	8.00	12.0
YrSold	1460.0	2007.815753	1.328095	2006.0	2007.00	2008.0	2009.00	2010.0
SalePrice	1460.0	180921.195890	79442.502883	34900.0	129975.00	163000.0	214000.00	755000.0

## Checking percentage of missing values
missing_info= round(df.isna().sum() * 100/df.shape[0], 2)
missing_info[missing_info > 0].sort_values(ascending= False)

PoolQC          99.52
MiscFeature     96.30
Alley           93.77
Fence           80.75
FireplaceQu     47.26
LotFrontage     17.74
GarageType       5.55
GarageYrBlt      5.55
GarageFinish     5.55
GarageQual       5.55
GarageCond       5.55
BsmtExposure     2.60
BsmtFinType2     2.60
BsmtFinType1     2.53
BsmtCond         2.53
BsmtQual         2.53
MasVnrArea       0.55
MasVnrType       0.55
Electrical       0.07
dtype: float64

# Getting column names having missing values
missing_val_cols= missing_info[missing_info > 0].sort_values(ascending= False).index
missing_val_cols

Index(['PoolQC', 'MiscFeature', 'Alley', 'Fence', 'FireplaceQu', 'LotFrontage',
       'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageQual', 'GarageCond',
       'BsmtExposure', 'BsmtFinType2', 'BsmtFinType1', 'BsmtCond', 'BsmtQual',
       'MasVnrArea', 'MasVnrType', 'Electrical'],
      dtype='object')

# Checking unique values in these columns
for col in missing_val_cols:
    print('\nColumn Name:',col)
    print(df[col].value_counts(dropna= False))

Column Name: PoolQC
NaN    1453
Gd        3
Ex        2
Fa        2
Name: PoolQC, dtype: int64

Column Name: MiscFeature
NaN     1406
Shed      49
Gar2       2
Othr       2
TenC       1
Name: MiscFeature, dtype: int64

Column Name: Alley
NaN     1369
Grvl      50
Pave      41
Name: Alley, dtype: int64

Column Name: Fence
NaN      1179
MnPrv     157
GdPrv      59
GdWo       54
MnWw       11
Name: Fence, dtype: int64

Column Name: FireplaceQu
NaN    690
Gd     380
TA     313
Fa      33
Ex      24
Po      20
Name: FireplaceQu, dtype: int64

Column Name: LotFrontage
NaN      259
60.0     143
70.0      70
80.0      69
50.0      57
        ... 
137.0      1
38.0       1
33.0       1
150.0      1
46.0       1
Name: LotFrontage, Length: 111, dtype: int64

Column Name: GarageType
Attchd     870
Detchd     387
BuiltIn     88
NaN         81
Basment     19
CarPort      9
2Types       6
Name: GarageType, dtype: int64

Column Name: GarageYrBlt
NaN       81
2005.0    65
2006.0    59
2004.0    53
2003.0    50
          ..
1927.0     1
1900.0     1
1906.0     1
1908.0     1
1933.0     1
Name: GarageYrBlt, Length: 98, dtype: int64

Column Name: GarageFinish
Unf    605
RFn    422
Fin    352
NaN     81
Name: GarageFinish, dtype: int64

Column Name: GarageQual
TA     1311
NaN      81
Fa       48
Gd       14
Ex        3
Po        3
Name: GarageQual, dtype: int64

Column Name: GarageCond
TA     1326
NaN      81
Fa       35
Gd        9
Po        7
Ex        2
Name: GarageCond, dtype: int64

Column Name: BsmtExposure
No     953
Av     221
Gd     134
Mn     114
NaN     38
Name: BsmtExposure, dtype: int64

Column Name: BsmtFinType2
Unf    1256
Rec      54
LwQ      46
NaN      38
BLQ      33
ALQ      19
GLQ      14
Name: BsmtFinType2, dtype: int64

Column Name: BsmtFinType1
Unf    430
GLQ    418
ALQ    220
BLQ    148
Rec    133
LwQ     74
NaN     37
Name: BsmtFinType1, dtype: int64

Column Name: BsmtCond
TA     1311
Gd       65
Fa       45
NaN      37
Po        2
Name: BsmtCond, dtype: int64

Column Name: BsmtQual
TA     649
Gd     618
Ex     121
NaN     37
Fa      35
Name: BsmtQual, dtype: int64

Column Name: MasVnrArea
0.0      861
NaN        8
72.0       8
108.0      8
180.0      8
        ... 
562.0      1
89.0       1
921.0      1
762.0      1
119.0      1
Name: MasVnrArea, Length: 328, dtype: int64

Column Name: MasVnrType
None       864
BrkFace    445
Stone      128
BrkCmn      15
NaN          8
Name: MasVnrType, dtype: int64

Column Name: Electrical
SBrkr    1334
FuseA      94
FuseF      27
FuseP       3
Mix         1
NaN         1
Name: Electrical, dtype: int64

# Replacing NaN with 'Not Present' for below columns
valid_nan_cols= ['PoolQC', 'MiscFeature', 'Alley', 'Fence', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'BsmtFinType2', 'BsmtExposure', 'BsmtFinType1', 'BsmtCond', 'BsmtQual']
df[valid_nan_cols]= df[valid_nan_cols].fillna('Not Present')

# Checking percentage of missing values again
missing_info= round(df.isna().sum() * 100/df.shape[0], 2)
missing_info[missing_info > 0].sort_values(ascending= False)

LotFrontage    17.74
GarageYrBlt     5.55
MasVnrType      0.55
MasVnrArea      0.55
Electrical      0.07
dtype: float64

# Checking if there is any relation between GarageYrBlt and GarageType
df[df.GarageYrBlt.isna()]['GarageType'].value_counts(normalize= True)

Not Present    1.0
Name: GarageType, dtype: float64

There are a total of 19 columns with missing values. Notably, PoolQC, MiscFeature, Alley, Fence, and FireplaceQu exhibit a particularly high percentage of missing values. It is crucial to examine these columns individually to determine whether these missing values are indeed indicative of actual data gaps or if they hold some meaningful information. Once we identify the nature of these missing values, we can proceed with the appropriate imputation technique. One approach is to utilize business knowledge for imputation, where NaN values can be replaced with values derived from relevant business logic. Alternatively, statistical imputation methods can be employed after performing a train-test split, allowing missing values to be filled based on statistical techniques.

# Getting column names having missing values
missing_val_cols= missing_info[missing_info > 0].sort_values(ascending= False).index
missing_val_cols

Index(['LotFrontage', 'GarageYrBlt', 'MasVnrType', 'MasVnrArea', 'Electrical'], dtype='object')

# Checking unique values in these columns
for col in missing_val_cols:
    print('\nColumn Name:',col)
    print(df[col].value_counts(dropna= False))

Column Name: LotFrontage
NaN      259
60.0     143
70.0      70
80.0      69
50.0      57
        ... 
137.0      1
38.0       1
33.0       1
150.0      1
46.0       1
Name: LotFrontage, Length: 111, dtype: int64

Column Name: GarageYrBlt
NaN       81
2005.0    65
2006.0    59
2004.0    53
2003.0    50
          ..
1927.0     1
1900.0     1
1906.0     1
1908.0     1
1933.0     1
Name: GarageYrBlt, Length: 98, dtype: int64

Column Name: MasVnrType
None       864
BrkFace    445
Stone      128
BrkCmn      15
NaN          8
Name: MasVnrType, dtype: int64

Column Name: MasVnrArea
0.0      861
NaN        8
72.0       8
108.0      8
180.0      8
        ... 
562.0      1
89.0       1
921.0      1
762.0      1
119.0      1
Name: MasVnrArea, Length: 328, dtype: int64

Column Name: Electrical
SBrkr    1334
FuseA      94
FuseF      27
FuseP       3
Mix         1
NaN         1
Name: Electrical, dtype: int64

# Replacing NaN with 'Not Present' for below columns
valid_nan_cols= ['PoolQC', 'MiscFeature', 'Alley', 'Fence', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'BsmtFinType2', 'BsmtExposure', 'BsmtFinType1', 'BsmtCond', 'BsmtQual']
df[valid_nan_cols]= df[valid_nan_cols].fillna('Not Present')

# Checking percentage of missing values again
missing_info= round(df.isna().sum() * 100/df.shape[0], 2)
missing_info[missing_info > 0].sort_values(ascending= False)

LotFrontage    17.74
GarageYrBlt     5.55
MasVnrType      0.55
MasVnrArea      0.55
Electrical      0.07
dtype: float64

# Checking if there is any relation between GarageYrBlt and GarageType
df[df.GarageYrBlt.isna()]['GarageType'].value_counts(normalize= True)

Not Present    1.0
Name: GarageType, dtype: float64

Initially, both GarageYrBlt and GarageType had a missing value percentage of 5.55%. However, after replacing the NaN values of GarageType with 'Not Available', we observed that the GarageYrBlt values were only NaN for those instances where GarageType was labeled as 'Not Available'. This indicates that if a garage is not available, there will be no corresponding value for 'GarageYrBlt'. As a result, it is reasonable to safely replace the NaN values of GarageYrBlt with 0.

# Imputing missing values of GarageYrBlt column
df['GarageYrBlt']= df['GarageYrBlt'].fillna(0)

# Changing data type of MSSubClass. MSSubClass: "identifies the type of dwelling involved in the sale", 
#is a categorical variable, but it's appearing as a numeric variable.
df['MSSubClass']= df['MSSubClass'].astype('object')

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1460 entries, 0 to 1459
Data columns (total 81 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Id             1460 non-null   int64  
 1   MSSubClass     1460 non-null   object 
 2   MSZoning       1460 non-null   object 
 3   LotFrontage    1201 non-null   float64
 4   LotArea        1460 non-null   int64  
 5   Street         1460 non-null   object 
 6   Alley          1460 non-null   object 
 7   LotShape       1460 non-null   object 
 8   LandContour    1460 non-null   object 
 9   Utilities      1460 non-null   object 
 10  LotConfig      1460 non-null   object 
 11  LandSlope      1460 non-null   object 
 12  Neighborhood   1460 non-null   object 
 13  Condition1     1460 non-null   object 
 14  Condition2     1460 non-null   object 
 15  BldgType       1460 non-null   object 
 16  HouseStyle     1460 non-null   object 
 17  OverallQual    1460 non-null   int64  
 18  OverallCond    1460 non-null   int64  
 19  YearBuilt      1460 non-null   int64  
 20  YearRemodAdd   1460 non-null   int64  
 21  RoofStyle      1460 non-null   object 
 22  RoofMatl       1460 non-null   object 
 23  Exterior1st    1460 non-null   object 
 24  Exterior2nd    1460 non-null   object 
 25  MasVnrType     1452 non-null   object 
 26  MasVnrArea     1452 non-null   float64
 27  ExterQual      1460 non-null   object 
 28  ExterCond      1460 non-null   object 
 29  Foundation     1460 non-null   object 
 30  BsmtQual       1460 non-null   object 
 31  BsmtCond       1460 non-null   object 
 32  BsmtExposure   1460 non-null   object 
 33  BsmtFinType1   1460 non-null   object 
 34  BsmtFinSF1     1460 non-null   int64  
 35  BsmtFinType2   1460 non-null   object 
 36  BsmtFinSF2     1460 non-null   int64  
 37  BsmtUnfSF      1460 non-null   int64  
 38  TotalBsmtSF    1460 non-null   int64  
 39  Heating        1460 non-null   object 
 40  HeatingQC      1460 non-null   object 
 41  CentralAir     1460 non-null   object 
 42  Electrical     1459 non-null   object 
 43  1stFlrSF       1460 non-null   int64  
 44  2ndFlrSF       1460 non-null   int64  
 45  LowQualFinSF   1460 non-null   int64  
 46  GrLivArea      1460 non-null   int64  
 47  BsmtFullBath   1460 non-null   int64  
 48  BsmtHalfBath   1460 non-null   int64  
 49  FullBath       1460 non-null   int64  
 50  HalfBath       1460 non-null   int64  
 51  BedroomAbvGr   1460 non-null   int64  
 52  KitchenAbvGr   1460 non-null   int64  
 53  KitchenQual    1460 non-null   object 
 54  TotRmsAbvGrd   1460 non-null   int64  
 55  Functional     1460 non-null   object 
 56  Fireplaces     1460 non-null   int64  
 57  FireplaceQu    1460 non-null   object 
 58  GarageType     1460 non-null   object 
 59  GarageYrBlt    1460 non-null   float64
 60  GarageFinish   1460 non-null   object 
 61  GarageCars     1460 non-null   int64  
 62  GarageArea     1460 non-null   int64  
 63  GarageQual     1460 non-null   object 
 64  GarageCond     1460 non-null   object 
 65  PavedDrive     1460 non-null   object 
 66  WoodDeckSF     1460 non-null   int64  
 67  OpenPorchSF    1460 non-null   int64  
 68  EnclosedPorch  1460 non-null   int64  
 69  3SsnPorch      1460 non-null   int64  
 70  ScreenPorch    1460 non-null   int64  
 71  PoolArea       1460 non-null   int64  
 72  PoolQC         1460 non-null   object 
 73  Fence          1460 non-null   object 
 74  MiscFeature    1460 non-null   object 
 75  MiscVal        1460 non-null   int64  
 76  MoSold         1460 non-null   int64  
 77  YrSold         1460 non-null   int64  
 78  SaleType       1460 non-null   object 
 79  SaleCondition  1460 non-null   object 
 80  SalePrice      1460 non-null   int64  
dtypes: float64(3), int64(34), object(44)
memory usage: 924.0+ KB

# Running SweetViz AutoEDA
sv_report= sv.analyze(df)
sv_report.show_notebook()

                                             |                                             | [  0%]   00:00 ->…

# Plotting numeric variables against SalePrice

numeric_cols= ['GrLivArea','GarageArea','TotalBsmtSF','1stFlrSF','TotRmsAbvGrd','YearBuilt','YearRemodAdd','MasVnrArea',
'BsmtFinSF1','LotFrontage','WoodDeckSF','2ndFlrSF','OpenPorchSF','LotArea']

sns.pairplot(df, x_vars=['GrLivArea','GarageArea','TotalBsmtSF','1stFlrSF','TotRmsAbvGrd'], y_vars='SalePrice', kind= 'reg', plot_kws={'line_kws':{'color':'teal'}})
sns.pairplot(df, x_vars=['YearBuilt','YearRemodAdd','MasVnrArea','BsmtFinSF1','LotFrontage'], y_vars='SalePrice', kind= 'reg', plot_kws={'line_kws':{'color':'teal'}})
sns.pairplot(df, x_vars=['WoodDeckSF','2ndFlrSF','OpenPorchSF','LotArea'], y_vars='SalePrice', kind= 'reg', plot_kws={'line_kws':{'color':'teal'}})

<seaborn.axisgrid.PairGrid at 0x2d60ae79b40>

# Box plot of catego

cat_cols= ['OverallQual','GarageCars','ExterQual','BsmtQual','KitchenQual','FullBath','GarageFinish','FireplaceQu','Foundation','GarageType','Fireplaces','BsmtFinType1','HeatingQC']

plt.figure(figsize=[18, 40])

for i, col in enumerate(cat_cols, 1):
    plt.subplot(7,2,i)
    title_text= f'Box plot {col} vs cnt'
    x_label= f'{col}'
    fig= sns.boxplot(data= df, x= col, y= 'SalePrice', palette= 'Greens')
    fig.set_title(title_text, fontdict= { 'fontsize': 18, 'color': 'Green'})
    fig.set_xlabel(x_label, fontdict= {'fontsize': 12, 'color': 'Brown'})
plt.show()

plt.figure(figsize=[17,7])
sns.boxplot(data= df, x= 'Neighborhood', y= 'SalePrice', palette= 'Greens')
plt.show()

plt.figure(figsize=(24, 12))
sns.heatmap(df.corr(),
            cmap = 'BrBG',
            fmt = '.2f',
            linewidths = 2,
            annot = True)

C:\Users\HP\AppData\Local\Temp\ipykernel_11432\813657993.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
  sns.heatmap(df.corr(),

<Axes: >

obj = (df.dtypes == 'object')
object_cols = list(obj[obj].index)
print("Categorical variables:",len(object_cols))

int_ = (df.dtypes == 'int')
num_cols = list(int_[int_].index)
print("Integer variables:",len(num_cols))

fl = (df.dtypes == 'float')
fl_cols = list(fl[fl].index)
print("Float variables:",len(fl_cols))

Categorical variables: 44
Integer variables: 0
Float variables: 3

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1460 entries, 0 to 1459
Data columns (total 81 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Id             1460 non-null   int64  
 1   MSSubClass     1460 non-null   object 
 2   MSZoning       1460 non-null   object 
 3   LotFrontage    1201 non-null   float64
 4   LotArea        1460 non-null   int64  
 5   Street         1460 non-null   object 
 6   Alley          1460 non-null   object 
 7   LotShape       1460 non-null   object 
 8   LandContour    1460 non-null   object 
 9   Utilities      1460 non-null   object 
 10  LotConfig      1460 non-null   object 
 11  LandSlope      1460 non-null   object 
 12  Neighborhood   1460 non-null   object 
 13  Condition1     1460 non-null   object 
 14  Condition2     1460 non-null   object 
 15  BldgType       1460 non-null   object 
 16  HouseStyle     1460 non-null   object 
 17  OverallQual    1460 non-null   int64  
 18  OverallCond    1460 non-null   int64  
 19  YearBuilt      1460 non-null   int64  
 20  YearRemodAdd   1460 non-null   int64  
 21  RoofStyle      1460 non-null   object 
 22  RoofMatl       1460 non-null   object 
 23  Exterior1st    1460 non-null   object 
 24  Exterior2nd    1460 non-null   object 
 25  MasVnrType     1452 non-null   object 
 26  MasVnrArea     1452 non-null   float64
 27  ExterQual      1460 non-null   object 
 28  ExterCond      1460 non-null   object 
 29  Foundation     1460 non-null   object 
 30  BsmtQual       1460 non-null   object 
 31  BsmtCond       1460 non-null   object 
 32  BsmtExposure   1460 non-null   object 
 33  BsmtFinType1   1460 non-null   object 
 34  BsmtFinSF1     1460 non-null   int64  
 35  BsmtFinType2   1460 non-null   object 
 36  BsmtFinSF2     1460 non-null   int64  
 37  BsmtUnfSF      1460 non-null   int64  
 38  TotalBsmtSF    1460 non-null   int64  
 39  Heating        1460 non-null   object 
 40  HeatingQC      1460 non-null   object 
 41  CentralAir     1460 non-null   object 
 42  Electrical     1459 non-null   object 
 43  1stFlrSF       1460 non-null   int64  
 44  2ndFlrSF       1460 non-null   int64  
 45  LowQualFinSF   1460 non-null   int64  
 46  GrLivArea      1460 non-null   int64  
 47  BsmtFullBath   1460 non-null   int64  
 48  BsmtHalfBath   1460 non-null   int64  
 49  FullBath       1460 non-null   int64  
 50  HalfBath       1460 non-null   int64  
 51  BedroomAbvGr   1460 non-null   int64  
 52  KitchenAbvGr   1460 non-null   int64  
 53  KitchenQual    1460 non-null   object 
 54  TotRmsAbvGrd   1460 non-null   int64  
 55  Functional     1460 non-null   object 
 56  Fireplaces     1460 non-null   int64  
 57  FireplaceQu    1460 non-null   object 
 58  GarageType     1460 non-null   object 
 59  GarageYrBlt    1460 non-null   float64
 60  GarageFinish   1460 non-null   object 
 61  GarageCars     1460 non-null   int64  
 62  GarageArea     1460 non-null   int64  
 63  GarageQual     1460 non-null   object 
 64  GarageCond     1460 non-null   object 
 65  PavedDrive     1460 non-null   object 
 66  WoodDeckSF     1460 non-null   int64  
 67  OpenPorchSF    1460 non-null   int64  
 68  EnclosedPorch  1460 non-null   int64  
 69  3SsnPorch      1460 non-null   int64  
 70  ScreenPorch    1460 non-null   int64  
 71  PoolArea       1460 non-null   int64  
 72  PoolQC         1460 non-null   object 
 73  Fence          1460 non-null   object 
 74  MiscFeature    1460 non-null   object 
 75  MiscVal        1460 non-null   int64  
 76  MoSold         1460 non-null   int64  
 77  YrSold         1460 non-null   int64  
 78  SaleType       1460 non-null   object 
 79  SaleCondition  1460 non-null   object 
 80  SalePrice      1460 non-null   int64  
dtypes: float64(3), int64(34), object(44)
memory usage: 924.0+ KB

unique_values = []
for col in object_cols:
    unique_values.append(df[col].unique().size)
plt.figure(figsize=(10,6))
plt.title('No. Unique values of Categorical Features')
plt.xticks(rotation=90)
sns.barplot(x=object_cols,y=unique_values)

<Axes: title={'center': 'No. Unique values of Categorical Features'}>

plt.figure(figsize=(30, 56))
plt.title('Categorical Features: Distribution')
plt.xticks(rotation=90)
index = 1

for col in object_cols:
    y = df[col].value_counts()
    plt.subplot(11, 4, index)
    plt.xticks(rotation=90)
    sns.barplot(x=list(y.index), y=y)
    index += 1

C:\Users\HP\AppData\Local\Temp\ipykernel_11432\4165315780.py:8: MatplotlibDeprecationWarning: Auto-removal of overlapping axes is deprecated since 3.6 and will be removed two minor releases later; explicitly call ax.remove() as needed.
  plt.subplot(11, 4, index)

sns.boxenplot(x=df["SalePrice"])

<Axes: xlabel='SalePrice'>

sns.lmplot(x = "SalePrice", y = "GarageArea",
           hue = "LotShape", data = df, legend = True,aspect=20/12)

<seaborn.axisgrid.FacetGrid at 0x2d67a1f59f0>

sns.boxenplot(data=df, x="SalePrice", y="MSZoning", scale="linear")

<Axes: xlabel='SalePrice', ylabel='MSZoning'>

g = sns.JointGrid(data=df, x="SalePrice", y="LotArea")
g.plot_joint(sns.scatterplot, s=100, alpha=.5)
g.plot_marginals(sns.histplot, kde=True)

<seaborn.axisgrid.JointGrid at 0x2d679d99c00>

sns.catplot(data=df, x="HouseStyle", y="SalePrice", aspect= 20/18)

<seaborn.axisgrid.FacetGrid at 0x2d673cfd1b0>

sns.catplot(data=df, x="SalePrice", y="Neighborhood", kind="box")

<seaborn.axisgrid.FacetGrid at 0x2d671021f30>

sns.catplot(data=df, x="SalePrice", y="BldgType", kind="box")

<seaborn.axisgrid.FacetGrid at 0x2d673cfd9c0>

#Scatter plot to find relation between GarageArea and SalePrice
sns.set_style('darkgrid')

plt.figure(figsize=(8,6))
sns.scatterplot(data=df, x='GarageArea', y='SalePrice', s=80, alpha=0.7, color="#008080")
plt.title("Sale Price vs. Garage Area", fontsize=18)
plt.xlabel("Garage Area", fontsize=12)
plt.ylabel("Sale Price", fontsize=12)
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
plt.show()

sns.lineplot(data=df, x="SalePrice", y="GarageArea")

<Axes: xlabel='SalePrice', ylabel='GarageArea'>

sns.distplot((df['SalePrice']))
plt.show()

C:\Users\HP\AppData\Local\Temp\ipykernel_11432\873051638.py:1: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot((df['SalePrice']))

# Distplot of log transformed SalePrice to reduce skewness.
sns.distplot(np.log(df['SalePrice']))
plt.show()

C:\Users\HP\AppData\Local\Temp\ipykernel_11432\2565266045.py:2: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(np.log(df['SalePrice']))

# Transforming 'SalePrice'
df['SalePrice_log_trans']= np.log(df['SalePrice'])

# Dropping ID Column and SalePrice
df.drop(['SalePrice','Id'], axis=1, inplace= True)
df.shape

(1460, 80)

df.shape

(1460, 80)

# Train-Test Split
y= df['SalePrice_log_trans']
X= df.drop('SalePrice_log_trans', axis= 1)

X_train, X_test, y_train, y_test= train_test_split(X, y, train_size= .7, random_state= 42)
df.shape

(1460, 80)

# Getting index values of train test dataset
train_index= X_train.index
test_index= X_test.index

# Performing Statistical Imputation for missing values in LotFrontage, MasVnrArea, MasVnrType, Electrical columns

df['LotFrontage'].fillna(X_train['LotFrontage'].median(), inplace= True)
df['LotFrontage'].fillna(X_train['LotFrontage'].median(), inplace= True)

df['MasVnrArea'].fillna(X_train['MasVnrArea'].median(), inplace= True)
df['MasVnrArea'].fillna(X_train['MasVnrArea'].median(), inplace= True)

df['MasVnrType'].fillna(X_train['MasVnrType'].mode(), inplace= True)
df['MasVnrType'].fillna(X_train['MasVnrType'].mode(), inplace= True)

df['Electrical'].fillna(X_train['Electrical'].mode(), inplace= True)
df['Electrical'].fillna(X_train['Electrical'].mode(), inplace= True)

# Getting object and numeric type columns
housing_cat= df.select_dtypes(include= 'object')
housing_num= df.select_dtypes(exclude= 'object')
housing_cat.describe()

	MSSubClass	MSZoning	Street	Alley	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	...	GarageType	GarageFinish	GarageQual	GarageCond	PavedDrive	PoolQC	Fence	MiscFeature	SaleType	SaleCondition
count	1460	1460	1460	1460	1460	1460	1460	1460	1460	1460	...	1460	1460	1460	1460	1460	1460	1460	1460	1460	1460
unique	15	5	2	3	4	4	2	5	3	25	...	7	4	6	6	3	4	5	5	9	6
top	20	RL	Pave	Not Present	Reg	Lvl	AllPub	Inside	Gtl	NAmes	...	Attchd	Unf	TA	TA	Y	Not Present	Not Present	Not Present	WD	Normal
freq	536	1151	1454	1369	925	1311	1459	1052	1382	225	...	870	605	1311	1326	1340	1453	1179	1406	1267	1198

4 rows × 44 columns

# 'Street','Utilities', 'CentralAir' have 2 unique data, so we are encoding with 0 and 1
df['Street']= df.Street.map(lambda x: 1 if x== 'Pave' else 0)
df['Utilities']= df.Utilities.map(lambda x: 1 if x== 'AllPub' else 0)
df['CentralAir']= df.CentralAir.map(lambda x: 1 if x== 'Y' else 0)

# Performing get_dummies
cat_cols= housing_cat.columns.tolist()
done_encoding= ['Street','Utilities', 'CentralAir']
cat_cols= [col for col in cat_cols if col not in done_encoding]
dummies= pd.get_dummies(df[cat_cols], drop_first=True)

C:\Users\HP\AppData\Local\Temp\ipykernel_11432\59568176.py:5: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)
  dummies= pd.get_dummies(df[cat_cols], drop_first=True)

# Checking all dummies
dummies.head()

	MSSubClass_30	MSSubClass_40	MSSubClass_45	MSSubClass_50	MSSubClass_60	MSSubClass_70	MSSubClass_75	MSSubClass_80	MSSubClass_85	MSSubClass_90	...	SaleType_ConLI	SaleType_ConLw	SaleType_New	SaleType_Oth	SaleType_WD	SaleCondition_AdjLand	SaleCondition_Alloca	SaleCondition_Family	SaleCondition_Normal	SaleCondition_Partial
0	0	0	0	0	1	0	0	0	0	0	...	0	0	0	0	1	0	0	0	1	0
1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	1	0	0	0	1	0
2	0	0	0	0	1	0	0	0	0	0	...	0	0	0	0	1	0	0	0	1	0
3	0	0	0	0	0	1	0	0	0	0	...	0	0	0	0	1	0	0	0	0	0
4	0	0	0	0	1	0	0	0	0	0	...	0	0	0	0	1	0	0	0	1	0

5 rows × 234 columns

# Concatinating dummies with housing_df dataframe and droping original features
print('housing_df before droping original valiables', df.shape)
print('shape of dummies dataframe', dummies.shape)
df.drop(cat_cols, axis=1, inplace= True)
df= pd.concat([df, dummies], axis= 1)
print('final shape of housing_df', df.shape)

housing_df before droping original valiables (1460, 80)
shape of dummies dataframe (1460, 234)
final shape of housing_df (1460, 273)

X_train=X_train.drop('Utilities',axis=1)

# Re-constructing Train-test data
X_train= df.iloc[train_index, :].drop('SalePrice_log_trans', axis= 1)
y_train= df.iloc[train_index, :]['SalePrice_log_trans']
X_test= df.iloc[test_index, :].drop('SalePrice_log_trans', axis= 1)
y_test= df.iloc[test_index, :]['SalePrice_log_trans']

# Performing scaling of numeric columns in training and test dataset using RobustScaler
num_cols= housing_num.columns.tolist()
num_cols.remove('SalePrice_log_trans')
scaler= RobustScaler(quantile_range=(2, 98))
scaler.fit(X_train[num_cols])
X_train[num_cols]= scaler.transform(X_train[num_cols])
X_test[num_cols]= scaler.transform(X_test[num_cols])

# Checking scaled features
X_train[num_cols].head()

	LotFrontage	LotArea	OverallQual	OverallCond	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	BsmtFinSF2	BsmtUnfSF	...	GarageArea	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	MiscVal	MoSold	YrSold
1452	-0.360825	-0.255342	-0.2	0.0	0.318533	0.186441	0.126743	0.109760	0.000000	-0.290195	...	0.045214	0.000000	0.003912	0.0	0.0	0.0	0.0	0.0	-0.090909	-0.50
762	0.020619	-0.041372	0.2	0.0	0.357143	0.254237	0.000000	-0.255872	0.000000	0.149603	...	0.143361	0.367391	0.070423	0.0	0.0	0.0	0.0	0.0	0.000000	0.50
932	0.144330	0.089208	0.6	0.0	0.328185	0.203390	0.478454	-0.272651	0.000000	0.854362	...	0.335245	0.000000	0.641628	0.0	0.0	0.0	0.0	0.0	-0.272727	-0.25
435	-0.278351	0.045983	0.2	0.2	0.231660	0.033898	0.000000	-0.003496	0.559168	-0.248137	...	0.072783	0.343478	0.133020	0.0	0.0	0.0	0.0	0.0	-0.181818	0.25
629	0.123711	-0.024995	0.0	0.0	-0.077220	-0.508475	0.410330	0.163591	0.546164	-0.117159	...	0.039700	0.382609	-0.105634	0.0	0.0	0.0	0.0	0.0	0.000000	0.00

5 rows × 35 columns

var_t= VarianceThreshold(threshold= .003)
variance_thresh= var_t.fit(X_train)
col_ind= var_t.get_support()

# Below columns have very low variance
X_train.loc[:, ~col_ind].columns

Index(['Utilities', 'MSSubClass_40', 'LotConfig_FR3', 'Neighborhood_Blueste',
       'Condition1_RRNe', 'Condition2_Feedr', 'Condition2_PosA',
       'Condition2_PosN', 'Condition2_RRAe', 'Condition2_RRAn',
       'Condition2_RRNn', 'RoofStyle_Shed', 'RoofMatl_Membran',
       'RoofMatl_Metal', 'RoofMatl_Roll', 'RoofMatl_WdShake',
       'Exterior1st_AsphShn', 'Exterior1st_BrkComm', 'Exterior1st_CBlock',
       'Exterior1st_ImStucc', 'Exterior1st_Stone', 'Exterior2nd_AsphShn',
       'Exterior2nd_CBlock', 'Exterior2nd_Other', 'Exterior2nd_Stone',
       'ExterCond_Po', 'Foundation_Wood', 'BsmtCond_Po', 'Heating_OthW',
       'Heating_Wall', 'HeatingQC_Po', 'Electrical_FuseP', 'Electrical_Mix',
       'Functional_Sev', 'GarageQual_Po', 'GarageCond_Po', 'PoolQC_Fa',
       'PoolQC_Gd', 'MiscFeature_Othr', 'MiscFeature_TenC', 'SaleType_Con',
       'SaleType_ConLI', 'SaleType_ConLw', 'SaleType_Oth'],
      dtype='object')

# Checking number of apperance of one of the attributes/categorical value in dataset
df_org.Functional.value_counts()

Typ     1360
Min2      34
Min1      31
Mod       15
Maj1      14
Maj2       5
Sev        1
Name: Functional, dtype: int64

# Removing above columns from train and test dataset
X_train= X_train.loc[:, col_ind]
X_test= X_test.loc[:, col_ind]

# Checking shape of final training dataset
X_train.shape

(1021, 228)

# Selecting few values for alpha
range1= [0.0001, 0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
range2= list(range(2, 1001))
range1.extend(range2)
params_grid= {'alpha': range1}

# Applying Ridge and performing GridSearchCV to find optimal value of alpha (lambda)

ridge= Ridge(random_state= 42)
gcv_ridge= GridSearchCV(estimator= ridge, 
                        param_grid= params_grid,
                        cv= 3,
                        scoring= 'neg_mean_absolute_error',
                        return_train_score= True,
                        n_jobs= -1,
                        verbose= 1)      
gcv_ridge.fit(X_train, y_train)

Fitting 3 folds for each of 1013 candidates, totalling 3039 fits

GridSearchCV(cv=3, estimator=Ridge(random_state=42), n_jobs=-1,
             param_grid={'alpha': [0.0001, 0.001, 0.01, 0.05, 0.1, 0.2, 0.3,
                                   0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 2, 3, 4,
                                   5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
                                   17, ...]},
             return_train_score=True, scoring='neg_mean_absolute_error',
             verbose=1)

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# Checking best estimator 
gcv_ridge.best_estimator_

Ridge(alpha=8, random_state=42)

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# Checking best MAE
gcv_ridge.best_score_

-0.09512679670344619

# Checking best MAE
gcv_ridge.best_score_

-0.09512679670344619

# Fitting model using best_estimator_
ridge_model= gcv_ridge.best_estimator_
ridge_model.fit(X_train, y_train)

Ridge(alpha=8, random_state=42)

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models = ["RidgeCV", "LassoRegression", "KNN_Regressor","CrossValidationKNN_Regressor", "DecisonTreeRegressor", "GradientBoostingRegressor", "RandomForestRegressor", "AdaBoostRegressor", "CatBoostRegressor", "PassiveAggressiveRegressor"]

r2_test = []
MSE_test = []
RMSE_test = []
MAE_test = []

# Evaluating on training dataset
y_train_pred= ridge_model.predict(X_train)
print(y_train_pred)
print( 'r2 score on training dataset:', r2_score(y_train, y_train_pred))
print( 'MSE on training dataset:', mean_squared_error(y_train, y_train_pred))
print( 'RMSE on training dataset:', (mean_squared_error(y_train, y_train_pred)**.5))
print( 'MAE on training dataset:', mean_absolute_error(y_train, y_train_pred))

[11.75456286 12.24804681 12.5815665  ... 11.72663893 12.09020199
 12.19111285]
r2 score on training dataset: 0.9201164432816552
MSE on training dataset: 0.012394417531144047
RMSE on training dataset: 0.1113302184096665
MAE on training dataset: 0.07563612980664897

for i in y_train_pred:
    print(np.exp(i))

127333.23892736257
208573.50785803667
291142.0289771967
233949.87795376248
166863.04583185492
203503.34497099518
122332.0620204194
183589.19251661166
227696.86992127984
131734.37104145886
161132.1820874082
124317.56127086365
180822.8331452242
243053.72921510044
161346.4390295674
86376.2241589058
205304.6279675122
327713.17929702875
299883.17138348985
231650.81925560845
180059.58582897903
138308.70888626226
114619.34214065524
170721.1874521089
197348.48032837224
157637.76072354472
489603.3100489214
190798.28274265424
190301.12400774434
86512.86860083792
240662.66085969048
104917.06339368118
113902.72006827156
117931.70974667094
112843.11234731445
210150.61287187415
247615.870891639
144283.1610560465
131689.60628359742
129285.22496801421
207891.67060923314
141653.5735063906
170748.54900350433
146660.9812955782
122954.33186160288
220437.82620822667
151048.18600044548
158003.81300205755
96100.15872073203
168649.20990224346
375655.9727959405
238051.95435489746
221564.39611087146
194524.48373768476
300656.07833451487
185404.1736217031
170145.41478155015
220851.68937875063
89381.17946384888
75771.10732038943
165990.10374492352
398301.14295547165
95015.12953537205
108954.94650384203
138652.235979504
166813.02707103392
143213.98306624906
198526.86935704068
162438.216358688
226828.73476031004
114303.53529422641
193065.29312496918
72673.2481780872
153351.0254491409
277427.5209316148
117820.66475894969
137339.43996490916
217501.9355057002
110840.58093970575
235655.74148289053
170953.15615889643
150178.7465879881
186999.38054705464
222242.37638257962
88297.22506277653
204280.77031132326
193268.35159216117
152691.69737041663
144165.95426086942
212896.87090785382
139194.36995012403
282302.1312581991
328417.7765376709
235627.8730167127
155581.21548942785
321329.85684471653
141166.74888329385
134034.60699648203
128355.72712678825
195551.7468504804
156142.77729096767
141844.5472848424
151553.3545077278
301357.2399161335
247066.04700627792
291432.7354219564
260066.3438965061
138963.77655388755
196783.1108801388
157544.3488404373
124283.5966834075
403050.8122478503
111100.97209761044
316385.0095980751
97146.31405962796
157027.84796040354
179271.72189098355
62377.127138012
142647.2997369733
193252.39537224502
242744.3978789793
162592.55630375684
250559.43558023946
131291.0831708077
177607.11685006242
317251.45135412173
157439.4809436702
339000.72340112063
160328.47121878518
247601.7086196421
158313.7310075776
160258.02746886268
79824.77503392253
220087.30465730446
95147.00832302736
136705.24566862412
166097.52558836227
235199.71318867462
161652.77924407975
244184.34991667225
246046.4920363751
168530.38204820603
200555.45216101286
392411.7604434961
146673.544134903
179761.51775917286
155109.90768065964
187781.20213387764
165827.28771246911
217587.8883567749
138065.10205858207
105158.85915524012
84134.67455471493
159508.5894999249
124167.3391550522
112752.83357925048
102329.07289949927
197229.09030590855
108677.39920425045
199442.26586424495
299205.5140742983
212784.02410743354
198309.62522174628
121173.49193814534
129194.67079470774
171214.1649558024
124190.5302151984
290007.94861351216
196295.59309345248
153279.95023328057
127957.65159059488
252450.28314336346
124503.45066356118
155783.4672959196
92380.89405592532
223990.7143119768
192911.73227199982
136296.60668032052
193445.7071881119
185091.36956004667
156870.08622563112
119412.73807865685
192917.19698317617
131816.10351425144
224088.96343747483
277769.73173624254
137212.96976862088
161249.64283335375
236291.09901754247
191377.73280635092
129737.45889947754
136575.63395478917
288178.7695713944
173835.31424159498
223254.73614463847
203217.77341228962
180153.57296724248
202243.80691192846
184204.88226045517
204249.89728672014
202901.3884399804
249995.24706154526
378099.82211114827
212826.92151695618
243400.88322354676
99115.91171849758
428330.4909361963
169087.6151596656
95810.71605646987
160752.72407410265
175027.040236309
111270.31725007521
350046.49379021727
197973.1740493789
165684.92810295883
185722.66231482118
280213.46046808857
190619.64760704094
464518.63501159224
99164.63753970523
133737.62030675056
140675.87671960273
164842.6845289202
55946.38821840021
159956.30167789344
170722.85947496383
131209.93173462703
209812.6267940058
175049.57754417995
127008.72600220358
161395.14747039863
145314.41415618602
157012.54699515263
200899.92091863233
126431.51137270898
221594.78476086643
311061.68826427066
240743.69635932904
177210.9817660315
179643.90056880162
136604.38252660606
370258.83135462855
134539.06404773865
151202.89225278725
166544.5776700128
115220.17244879034
243634.8328173374
106165.93754394603
221356.09599476933
130578.01615777295
181479.37250153293
237890.94799234645
257608.7890440229
167703.24423448692
173607.8629347183
292598.98589388904
189431.46537007284
156719.47478956694
256536.56786210113
253799.66735926602
297143.21345105494
347926.2830335611
111366.15589408325
190645.22171773686
96909.64452071534
189827.08100270518
213648.02183876056
209507.149716428
119588.73405832717
296779.3756383833
126261.71438993703
131258.99544016828
118219.65493213359
80337.52600193527
94073.72800476242
324327.76472588873
130702.77784202203
262842.66922750656
219342.72391759683
129731.07436020143
177760.49283609004
191396.9076226018
95470.30803216605
94886.4125880223
192685.5962545868
258680.06240657353
140999.5914332024
88135.35436788447
256864.40445217353
296106.8086297257
272374.356680676
167359.58894048815
123303.84406363103
189372.4399450568
229620.2563967344
173217.4518541458
189947.19322472383
115234.60899540744
146403.54097737488
161233.21239271358
146733.22640471382
120175.99226769523
155992.98059803832
157458.45500806285
119806.60175208379
170041.83931770644
265270.9747107887
246403.31464142108
205201.8086381698
88214.40806046009
94941.51230572847
223543.70176925266
183150.8878503884
116553.33802469946
338914.7877671788
169840.6802949525
155983.7342988236
170468.22186086228
166569.2437869317
225228.9578309499
215455.55541662232
164026.93576676128
149816.4857449352
108365.123608754
171205.80277015682
172148.87311971257
281758.6282737162
218860.02133585306
199195.8576383195
230712.042354304
260929.17095953782
108513.09767919155
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112079.48922284952
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312240.1442656054
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132805.3448142238
122806.85923127246
114850.14367090308
202743.1567993696
217126.09689713575
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387293.99697008455
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186356.868199099
112992.5289826745
467146.02055333345
133245.56333275462
236720.03710583353
133729.3918090132
268921.79607556615
134220.8568031534
204693.80911042288
131757.37089084258
189000.7459251834
126347.01254039623
216295.24471097547
229674.74649420963
175412.82925838375
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129178.73525283203
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102599.46175874135
220103.449608534
193365.2869950086
116364.01114272831
205374.27247212295
188961.69449705337
206720.1743992551
133430.64300486143
142228.9964676947
157800.33368826535
116370.53914498712
88392.23402410917
189112.90961023077
136460.97271190988
254266.3291717111
227313.43749890587
84962.47820916511
124134.57777693096
192881.5789495889
174761.4646521611
135526.11958120912
210482.92874596128
223444.79832697133
162985.35993243303
169495.3896662614
146413.13251618875
437805.8806474594
132176.1707794561
524721.2541189467
277379.1235714522
79720.3824401706
109004.44346631248
195928.85629899002
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245635.57288589064
120967.33240008184
146386.88128674225
126372.66080303612
182116.7359181676
232150.88958876274
112872.71680416982
178430.47423034
105902.63474415688
205291.60680178588
217972.99179965912
133266.75167184288
137130.52749171932
284335.2037351825
189618.66425739517
188404.3892873555
137113.92340462888
138229.5363610561
118717.15499554048
167819.939573369
85518.95427231248
206715.89253059123
169698.53941030183
209260.88819575415
157882.94405270615
141087.75717474538
147974.1287833321
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104838.29965334428
159119.60234341264
144979.52447741164
180840.0849604351
210526.7072758717
104139.39461161837
97110.32506542935
252270.04804605944
182141.59740143947
158921.45448837904
121230.46756826536
110432.59472031164
96137.88219182332
176980.88488326632
325021.9936075194
148431.91644645404
127903.37739169883
260749.85970688742
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446321.2325086422
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62057.38760685618
246283.58819558684
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354965.3890709491
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204935.51144813825
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130274.88637552533
258534.58348742442
83086.40167458849
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136574.0296951808
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344650.1049653601
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251191.43093011327
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141210.0572844169
209438.46109374936
90042.48225942747
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155571.79771747708
228730.15080326475
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112903.17433823016
78233.31980605958
171886.99511942442
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338424.28266169806
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312236.12904221297
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383992.38091716525
210177.4912038266
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205023.74163448575
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316109.76407667197
187609.91727836904
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163812.22434987212
162842.14106234672
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119378.29072182837
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99630.75509014643
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188973.45660835804
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285577.2241591899
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144115.1941220344
71483.6762138788
154851.93845513853
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219301.7672824815
270479.1885862903
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112060.53514999518
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120273.77745727723
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100071.39555299781
139044.38283223132
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218377.65214428448
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84428.13952107598
143827.1659595985
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202374.9130645455
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154985.39064478298
172393.72011398818
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236960.5415898068
223009.7442168931
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132103.02493465447
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91579.02191288304
210738.6680891529
364001.40367379255
151198.52480488137
179645.679269342
215304.54771378732
135864.32273276694
97920.0062945486
193114.50405882555
182786.41237657884
122157.2967806471
69377.15896295548
94632.53186228566
297475.24758813245
314937.31686997943
245731.91191651873
125366.48969229634
131324.39250405907
222665.84656582066
101295.0170066642
132841.03809726564
111677.01715176234
140205.40629510843
220654.25093282008
211700.9154235758
171106.38962124012
87657.72064663903
94596.09859354405
152327.87808241355
126353.66040064936
166556.3855460658
136476.40165660373
271883.6628558678
158731.85581086375
135468.3195116327
137369.8016804096
264142.25893427065
139630.0344740951
243084.69749925067
142244.0208264484
238155.3496842972
126927.49113306444
81788.8665481538
172119.62556032476
114189.37060644176
110051.23217212208
125097.283073282
108127.29766429603
157432.0860527658
171552.20951208338
185586.85082796167
180633.9919311732
208290.80460412198
352764.7251064748
219396.74789612996
162318.38620896076
320839.2734265796
212939.6818262936
126720.8898678204
215684.39173632878
211410.2408460766
183850.03032122055
256418.14832361127
134137.62635348967
211896.2600671202
119171.0663173729
132034.23477962503
134317.3879070568
257880.56080133579
244746.19119817016
117648.53806079608
241748.3233245022
253142.30082951623
165441.6652304304
179968.511947848
236654.75151925778
196055.61514509216
349832.517515057
136767.32979285961
160398.39951461548
122751.42906638271
137090.19554811323
275863.2846874831
99247.67961753777
116695.16700361457
293415.71231366176
128577.2787483252
189371.4531241025
88876.13097274888
151540.05578000937
258604.190470191
145864.40329839312
117808.02963986737
158017.79384584646
125018.83532154332
155375.8730563356
163898.9926327793
142665.17626546227
196769.2220238655
184676.13242710778
214605.54196140476
170718.92394539568
143654.1063308217
173832.20878961924
139960.22797397707
121754.78202735564
151583.94520099694
301404.1679650813
112474.50218513368
126630.60585848268
287493.16232288576
193565.76090294693
313104.7326393013
148364.19789887665
254554.81761378172
168178.53389632003
130395.42946206963
185335.31520186606
152600.46742820012
160797.5839369742
160830.88711930008
174325.43988353192
138158.27082962656
169465.87725981144
91134.33008401401
132653.56995203788
105873.98043434956
200336.97343380903
95039.26511621474
137196.64628916496
185813.16220580792
153995.74929254706
238834.23321896885
115196.64760826579
338465.6538434029
252564.63256164832
118746.23697765308
114477.16935899772
278595.13521606417
82778.52500030688
99702.70126841015
113507.05273318476
131050.85917444719
150757.14596646547
222949.01862775092
207973.44517623767
148864.5715688984
128689.82392821084
174167.6589839884
198877.55736987398
148856.82356079482
196666.5025227227
135697.45934838592
338834.7522067507
112753.1011563246
108484.15301517973
228269.67418935415
145455.24052115675
129066.68967841541
93522.6969384724
155682.17943342178
217792.32358293742
89061.62460406826
224444.21183297556
145169.94451135126
212902.37554200497
156203.97385914906
171803.2069392909
176092.987375389
139853.65771972
303806.21819781815
205836.8670684246
161934.93731652314
171308.2356044546
133171.63856739804
215300.72866625464
142956.02883304976
152421.16767157658
233126.99532689722
127153.22051376446
167050.9671365788
245069.98449979964
195673.9874401766
157641.46242584524
123966.72739496906
159631.86472539807
388720.7203065679
100801.94500779663
209695.49690865673
347351.4412297107
130280.2749531233
128256.00427898984
208255.0442582803
128489.49639753731
108566.706891431
188140.4218384584
272344.65447417006
153270.6027204605
174340.103966491
149527.85884163203
209960.02876500387
112242.8855114362
170949.8870213307
143645.82866784168
285112.1134283608
245764.48091558408
230576.42921863476
95689.29163879309
226455.979585321
202238.04754516517
286695.809520432
133623.83081405403
141253.9765557805
192708.70492903393
139392.25405029533
334513.2855562916
107556.92385587745
224328.63066439822
159656.61966458117
327635.9900112592
104028.53233745246
139108.17902289357
211894.7171029414
129479.62035558932
273396.39984599967
170920.91004539037
206133.93440532358
151190.39658290517
195822.27583614175
244449.0820941035
148484.36574059108
221090.49813155615
178454.44677118503
127050.46987302776
56948.757100658055
127549.86063553474
205582.07225218997
192228.15115199488
155562.4421696797
117048.78653426451
201142.5742513097
207178.21298729954
350110.74876286654
282664.874307423
525382.0812306624
110273.07465652331
206910.7884104419
180265.54382261907
103159.52472691388
153709.25499017796
166311.94711511422
146247.90734437137
141668.94397711245
168359.133977107
97516.07988487264
290463.84699975664
186050.5119484194
142436.3266854606
123826.77947446151
178118.08150642234
197030.30893066962

# Evaluating on testing dataset
y_test_pred= ridge_model.predict(X_test)
print( 'r2 score on testing dataset:', r2_score(y_test, y_test_pred))
print( 'MSE on testing dataset:', mean_squared_error(y_test, y_test_pred))
print( 'RMSE on testing dataset:', (mean_squared_error(y_test, y_test_pred)**.5))
print( 'MAE on testing dataset:', mean_absolute_error(y_test, y_test_pred))

r2_test.append(r2_score(y_test, y_test_pred))
MAE_test.append(mean_absolute_error(y_test, y_test_pred))
MSE_test.append(mean_squared_error(y_test, y_test_pred))
RMSE_test.append(mean_squared_error(y_test, y_test_pred)**.5)

r2 score on testing dataset: 0.8913491323321061
MSE on testing dataset: 0.018390937818633723
RMSE on testing dataset: 0.13561319190489443
MAE on testing dataset: 0.09439608138185188

for i in y_test_pred:
    print(np.exp(i))

150735.2173466355
327083.6638099437
96828.74185150988
162213.49177661867
333003.0906641252
82119.17115822945
246776.8793595377
143813.03171055517
80459.13839639693
140966.8197294994
139909.6769476796
120237.35939387666
98521.76639556917
218672.8270382295
174030.23173920123
134009.15258230607
193466.61148764595
127894.71654342613
103516.09896505387
212001.78806138437
169321.41681099503
200757.1925063746
185820.5063071731
136825.27477528737
202915.0689299567
156158.9275426897
198881.01262352554
108490.13753158116
175423.3374904713
206394.63228826024
129340.11496624025
275593.3519763146
196043.54456802664
112148.08588833177
273206.40275152907
148552.15697685102
142181.0277276108
205171.31940382672
329648.6711368386
105422.3014634065
140097.5735545357
229478.4813055007
113332.4093851592
331367.5416364594
131502.51098866382
134612.2433938876
112726.95181570157
131246.85942803763
430274.5893622336
123821.93711216956
119505.40611666601
209962.93247040347
107712.43084695209
270216.6887177057
168483.57812665185
217754.95932222583
218209.58715644127
165029.19414189967
131846.05570798888
106627.82895726095
73698.51926582218
172978.65761425463
307429.3001049602
243578.7029332213
317854.2242451331
192822.8138043959
105068.81912743968
321353.4006618814
112605.9695346746
167755.61207695858
122764.96412114693
128758.31137518657
116215.89293211972
86087.2489417939
428813.05817903206
193933.15046739177
286967.19607424986
330921.3946769626
151133.21300143993
115759.0088801804
115763.09987759333
70076.97897475059
112329.93807792499
100750.50014535431
159206.24377671126
129938.66521637306
254753.15410393968
213808.07599571938
148143.47958357743
201284.21212184694
122751.86637328789
144626.10507313142
140224.09876903603
270874.52557466703
103787.84517303946
192695.5059243658
178791.57103997006
183452.6932060958
193038.83072526733
275387.5451828769
143781.44093714503
210348.91576874035
231749.270609864
137202.52141454775
184136.29440174377
186492.14810703366
160960.3923040032
271971.25705299474
147989.41078091497
208174.1516403631
53817.272334463414
116448.22579351619
146507.58479341987
142454.70812192463
198678.296021018
113719.0078151672
109109.74541743049
117194.3023909564
115724.90840327597
269856.606787931
127856.71113586493
146820.97196094596
179402.88323772795
190037.31479794427
188703.71606931722
133635.39218410745
231163.54489051373
94922.85470912547
151452.28342292266
199132.96306083264
190769.32166558225
324972.9378374554
186683.25206678157
121500.779012891
73791.99410811244
370383.1573755339
323411.19378962915
139358.35229984092
216310.63824827355
587942.2756609976
336719.0929464394
134319.33640599015
174717.5940497322
154992.2617099829
120294.94440988354
125749.59214120005
245972.057988954
186289.13889458927
126830.53662916987
68125.84797854928
120504.06935816791
134535.59761844203
244773.52633199107
148084.72149490102
74641.70166590126
120428.95621701163
116664.61190901743
134976.34291549562
91729.5139385925
136245.5386042893
202433.12452094426
115531.11317185454
292108.83274898486
144074.4099222766
114624.36389108787
119378.69278935707
253319.65674007646
306031.7540850738
459176.4122431546
222185.31884008416
379130.0284194708
88158.75472929257
112931.69362491797
155716.14790742233
304645.1134859151
116984.11403564444
119229.40554962863
217421.49780789603
125943.54741839814
159301.42122674827
192088.3299847548
99612.00983700246
126021.6351191492
142856.28633333102
246545.51959904356
115889.826084962
275585.44684241194
223062.2048039057
202304.64258045782
86911.57450822573
113846.53142805534
101130.92522053103
147508.26566810143
132211.36918780327
198555.3650401231
168201.3476827777
211873.18666337218
96942.49630405079
207864.18430877084
142821.74649795605
234993.24406061348
212517.97097463114
113959.1762003826
294836.06916358176
199935.45923070674
126661.50392500096
229001.79980670544
130225.22243716735
131492.8965521864
115170.50522967547
223706.45456332408
160206.34808415535
110154.801911343
162899.74758158685
220203.64753636738
241470.56183697446
210572.51584651545
133733.43211846423
124941.5222725225
132964.96238147657
135671.42175381293
227776.14669684472
193953.3111584902
100253.27666038697
231971.23961545934
136043.6713398109
97523.32764253029
105337.84205491914
160346.92386827464
107288.90463196131
101457.08810600481
174878.7812575781
122288.28312562015
117806.28540853807
227948.1509806138
147555.8926637416
201317.4857197945
156008.0508989371
234257.99701001612
129663.33331152564
105593.94120617902
250486.59408730073
218593.02719391102
468969.0003913071
197112.273389027
116879.06308613627
149268.76532888075
176364.29317683898
140290.3625389415
99879.75366916142
168551.22951677488
180560.39104543807
147215.2487514563
97164.41266546008
146189.63269233008
144906.8208619076
114441.95768811267
117875.70660239753
176058.17571147988
243317.80197172915
286727.62354087044
192710.92036343232
132797.26237854478
227281.42211742635
332348.79916841444
227818.14894061518
150819.7265604407
140349.16800388205
120538.46667853782
191661.86036278235
392726.82859860716
232301.8302559192
234822.50020278618
92309.35973311077
95653.07868523819
134900.15299986754
130082.57991457354
272321.2105241992
219303.9567029591
132687.8411178693
202480.5680651995
91645.5696778579
195105.3182812641
109554.55117372019
297052.0532487567
173792.63170505513
205261.11892576326
109370.19378191044
262084.7816807495
206585.72169482737
117818.6648618387
122910.64874061522
131998.3640963936
179594.37758856342
87661.7784974647
162276.08026935346
167858.1211098538
137522.10067338144
180277.15029511356
113909.88090744325
186187.56980169375
224806.47088545258
131271.5646192059
130466.8094944758
171837.89846524404
191480.60162570266
148098.95870182736
211733.8204216473
212399.5504587129
114844.35470790976
131333.26770818143
175362.23615365478
97074.31775092235
200886.06320338298
124744.13310439304
179822.3896325833
186717.4994681763
171725.41205348162
297099.0366806703
79810.73269177122
216335.66179885523
139719.48198529784
136728.36389612043
81160.13188784542
196380.91923064587
145974.9704653123
138184.56997726654
225082.27363405036
149750.2083013327
103791.09672887235
144745.72666205076
136471.63961238053
150829.31979133794
195139.2773015012
153211.3953989849
126496.51959231241
167430.7506966108
87801.01010980232
73475.13971658582
202150.5219939303
194180.3256744107
144039.61031668872
126276.54977967519
186423.005787735
231961.888797617
356324.42468440486
355578.69036073005
106297.79777748857
227452.05328500335
124664.4942216886
234460.4579804484
324170.6068103444
272085.6393480761
173547.81830268915
224848.12497194076
127701.87948101177
121237.99721900608
86412.46133776703
213111.59349389016
327895.3326493224
186025.5506239508
127170.6144340355
224793.45960263783
234035.39091388002
133200.09871961412
190724.10654111355
157424.23382510935
108758.39684437477
124710.12414100043
152818.35015453215
121520.50643196478
178402.82666134587
116438.41297767674
204133.8357710124
182086.58149003366
130912.47800258629
222882.00272039342
190525.55047814787
95845.69083560685
203478.52614728268
142473.1259137855
176234.7410473875
193489.60402699368
156529.77964211916
126309.29799173087
232998.36790822257
138631.11971559774
122474.14882826185
265135.292584789
171526.01509876113
122460.67438792865
132315.0132723657
142346.41582536913
351155.9579862824
137965.90198626736
419282.04756298877
127587.32771252359
196620.68857431458
175209.22681100023
131212.35227614423
167810.59557378423
373032.9477611025
101298.79389224389
157792.31010982458
143877.74818413498
99438.15953428613
182056.29643395919
130268.21661024766
211274.568786759
214805.52056333103
245990.80985750098
216362.0177583791
100261.21978645919
323927.4783208876
153877.35357168005
343255.8822603205
132615.24259994813
354942.9677982906
264661.2095157655
125454.68797940049
211195.42606899797
270167.29726576287
217193.14648219672
182266.94623226544
284014.1183189743
116410.8584928174
226141.7552369045
185789.48807247533
215685.97398544697
226791.17471556904
118566.80471732437
149648.00927417117
158091.21568782863
132876.084697309
126090.14264508094
204190.98836965678
131243.08235651837
414117.213085483
177627.6197748688

# Ridge coefficients
ridge_model.coef_

array([-2.68486138e-02,  3.25984187e-02,  7.67999217e-03,  2.19070944e-01,
        1.27389413e-01,  7.68847213e-02,  6.90005647e-02,  1.45548621e-05,
       -1.00769981e-02,  2.41648538e-02,  1.26788683e-02,  1.08076557e-02,
        6.99045317e-02,  9.05899110e-02,  1.06628751e-01,  1.82432938e-04,
        1.38964623e-01,  4.31122559e-02,  3.90062159e-03,  8.52713813e-02,
        4.45029301e-02,  5.05283578e-02, -4.62548322e-02,  9.83769993e-02,
        3.62528011e-02,  1.57014922e-02,  1.21569048e-01,  5.46275531e-02,
        4.93926839e-02, -7.11339009e-03,  2.88382885e-02,  2.69050509e-04,
        5.48497953e-02, -1.20117916e-04, -3.47185146e-03,  1.62497468e-02,
       -1.00239050e-02, -8.82321522e-02, -6.69858563e-03,  8.03367994e-03,
       -1.10268258e-02,  4.30327482e-02,  2.62107447e-02, -6.20766071e-03,
        4.52784252e-03, -6.64061581e-03, -2.60787175e-02, -7.84081439e-02,
       -2.02387769e-02, -1.01941404e-02,  4.35262269e-02,  1.33273910e-02,
        3.89371192e-02, -9.51557034e-03, -2.41207552e-02,  4.40509615e-02,
        2.49490273e-02, -5.44378010e-02, -2.44175106e-03,  8.15345797e-02,
        3.28624045e-02,  5.96515707e-02,  3.25897930e-02, -3.81416500e-02,
       -1.46270453e-02,  1.57822895e-03,  1.17632622e-02, -5.30470682e-03,
        2.64215501e-02,  3.91088889e-02, -1.89016845e-02,  8.26779255e-02,
       -6.92562205e-02, -3.12183577e-02, -4.30975525e-02, -7.93451573e-02,
       -2.32400897e-02, -3.41804667e-02,  1.38885111e-02, -1.85211778e-02,
        6.88909570e-02,  7.93038850e-02, -3.96363282e-02, -2.78036957e-02,
       -2.41758275e-02, -9.70326211e-03,  3.64902074e-02,  1.13146011e-01,
        5.16089031e-03,  3.22502121e-02,  2.20332911e-03,  5.47113701e-02,
        6.85172128e-03, -7.44529994e-03, -3.58050471e-02,  2.42455280e-02,
        1.07718113e-02,  6.67939311e-02, -8.26948217e-03, -6.64061581e-03,
       -6.73367586e-02, -5.73888798e-02,  1.74873846e-02,  3.91017522e-02,
       -1.72989314e-02,  1.22285381e-02, -2.72121605e-02, -2.19779472e-02,
       -8.41581125e-03, -4.10320521e-02, -1.18748574e-02, -2.68861013e-02,
        2.54209522e-02,  2.71363173e-02,  2.29451919e-02,  4.83864592e-02,
        9.16364124e-02,  1.73203289e-03, -2.05187832e-02,  1.46734906e-02,
        1.25317200e-02, -1.78487288e-02, -5.19926718e-03, -3.65685002e-02,
       -1.84778549e-02, -3.56449953e-02, -5.14538908e-03,  1.16782629e-02,
        4.18363253e-03,  3.85791625e-02, -1.29277877e-02, -2.08613278e-02,
       -1.86895291e-02, -4.40237076e-03,  3.39652024e-02, -1.88012398e-02,
        1.13271360e-02,  8.36534927e-03,  2.45415730e-02, -8.28852360e-03,
        4.82706456e-03, -1.76668608e-02, -1.60984602e-02, -3.49809990e-03,
        1.42257081e-02,  1.66119098e-02,  4.52983537e-02, -1.15018027e-02,
        9.92988114e-03, -4.15802973e-02, -5.85544258e-02, -2.55791409e-02,
       -7.95134627e-02,  4.33544405e-02, -2.55791409e-02,  5.87214763e-02,
        5.45956833e-02, -1.01162985e-02, -2.07248832e-02, -2.55791409e-02,
       -6.21031836e-03,  1.93632295e-02, -1.87685253e-02, -2.55791409e-02,
        1.44113218e-03, -4.99308803e-02, -2.00349920e-02,  7.83523666e-03,
        1.00696330e-03, -2.55791409e-02, -5.58322032e-03,  9.61800546e-03,
        2.29154827e-02,  4.59712787e-02, -5.01517788e-02, -3.38545126e-02,
       -2.68944733e-02, -2.24606606e-02, -4.47035091e-03,  1.00402531e-03,
       -1.74651192e-02, -5.45682494e-02, -6.86612916e-02, -6.83706740e-02,
        1.19231754e-02,  5.68437034e-03, -7.61199713e-03,  5.47493453e-02,
       -2.36024908e-02, -7.73025457e-03, -4.50070068e-02, -2.04708996e-02,
        5.16088738e-03,  2.88619344e-02,  9.18994326e-03,  1.40302450e-02,
       -2.02747668e-02,  1.49064814e-02, -1.66274987e-02, -1.66274987e-02,
        7.40472825e-03, -7.94470159e-03, -2.16196064e-02,  1.61777832e-02,
       -1.66274987e-02, -1.96639788e-02, -3.17485529e-02, -9.69551029e-05,
       -1.66274987e-02, -8.41765529e-03,  1.38405398e-02,  2.21614254e-02,
        3.11884961e-02, -3.58279760e-02, -4.48912605e-03, -1.28962890e-02,
       -2.45060636e-03,  1.63913716e-02,  3.62826396e-03,  2.63281115e-02,
        5.12683485e-02,  2.01528220e-02, -1.78403699e-02,  1.76623635e-02,
        6.80454356e-02, -7.34906196e-03,  3.81073855e-02,  2.87768795e-02])

# Ridge intercept
ridge_model.intercept_

11.666728523280653

# Top 10 features with double the value of optimal alpha in Ridge
ridge_coef= pd.Series(ridge_model.coef_, index= X_train.columns)
top_25_ridge=  ridge_coef[abs(ridge_coef).nlargest(25).index]
top_25_ridge

OverallQual             0.219071
GrLivArea               0.138965
OverallCond             0.127389
GarageCars              0.121569
Neighborhood_StoneBr    0.113146
2ndFlrSF                0.106629
TotRmsAbvGrd            0.098377
Exterior1st_BrkFace     0.091636
1stFlrSF                0.090590
MSSubClass_30          -0.088232
FullBath                0.085271
Neighborhood_Crawfor    0.082678
LandContour_HLS         0.081535
BsmtQual_TA            -0.079513
Neighborhood_MeadowV   -0.079345
Neighborhood_NridgHt    0.079304
MSSubClass_160         -0.078408
YearBuilt               0.076885
CentralAir              0.069905
Neighborhood_Edwards   -0.069256
YearRemodAdd            0.069001
Neighborhood_NoRidge    0.068891
KitchenQual_TA         -0.068661
Functional_Maj2        -0.068371
SaleCondition_Alloca    0.068045
dtype: float64

Lasso Regression

# Applying Lasso and performing GridSearchCV to find optimal value of alpha (lambda)

params_grid= {'alpha': range1}
lasso= Lasso(random_state= 42)
lasso_gcv= GridSearchCV(estimator= lasso, 
                        param_grid= params_grid,
                        cv= 3,
                        scoring= 'neg_mean_absolute_error',
                        return_train_score= True,
                        n_jobs= -1,
                        verbose= 1)

lasso_gcv.fit(X_train, y_train)         

Fitting 3 folds for each of 1013 candidates, totalling 3039 fits

GridSearchCV(cv=3, estimator=Lasso(random_state=42), n_jobs=-1,
             param_grid={'alpha': [0.0001, 0.001, 0.01, 0.05, 0.1, 0.2, 0.3,
                                   0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 2, 3, 4,
                                   5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
                                   17, ...]},
             return_train_score=True, scoring='neg_mean_absolute_error',
             verbose=1)

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# Checking best estimator 
lasso_gcv.best_estimator_

Lasso(alpha=0.001, random_state=42)

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# Checking best MAE
lasso_gcv.best_score_

-0.09436232253377737

range3= [0.00005, 0.00006, 0.00007, 0.00008, 0.00009, 0.0001, .0002, .0003, .0004, .0005, .0006, .0007, .0008, .0009, .001]
params_grid= {'alpha': range3}
lasso_gcv= GridSearchCV(estimator= lasso, 
                        param_grid= params_grid,
                        cv= 3,
                        scoring= 'neg_mean_absolute_error',
                        return_train_score= True,
                        n_jobs= -1,
                        verbose= 1)

lasso_gcv.fit(X_train, y_train)     

Fitting 3 folds for each of 15 candidates, totalling 45 fits

GridSearchCV(cv=3, estimator=Lasso(random_state=42), n_jobs=-1,
             param_grid={'alpha': [5e-05, 6e-05, 7e-05, 8e-05, 9e-05, 0.0001,
                                   0.0002, 0.0003, 0.0004, 0.0005, 0.0006,
                                   0.0007, 0.0008, 0.0009, 0.001]},
             return_train_score=True, scoring='neg_mean_absolute_error',
             verbose=1)

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# Checking best estimator 
lasso_gcv.best_estimator_

Lasso(alpha=0.0006, random_state=42)

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# Fitting model using best_estimator_
lasso_model= lasso_gcv.best_estimator_
lasso_model.fit(X_train, y_train)

Lasso(alpha=0.0006, random_state=42)

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# Evaluating on training dataset
y_train_pred= lasso_model.predict(X_train)
print( 'r2 score on training dataset:', r2_score(y_train, y_train_pred))
print( 'MSE on training dataset:', mean_squared_error(y_train, y_train_pred))
print( 'RMSE on training dataset:', (mean_squared_error(y_train, y_train_pred)**.5))
print( 'MAE on training dataset:', mean_absolute_error(y_train, y_train_pred))

r2 score on training dataset: 0.9122892305257844
MSE on training dataset: 0.013608856985103421
RMSE on training dataset: 0.11665700572663187
MAE on training dataset: 0.0781980493732077

for i in y_train_pred:
    print(np.exp(i))

128966.05444367189
208206.76687081772
274520.10213752516
227055.52430423358
157620.3312142239
203486.6048119059
123311.17173587829
180952.9142025229
227519.44184347914
131853.69276232796
166215.7965893261
123101.40938621352
177622.51609312906
243569.9547447668
147252.66494665912
86734.89096827968
207814.06600087948
338692.1024370229
302446.9969542423
243668.56190705576
171422.51951022423
137168.72362794698
111811.03289816197
171498.59085370944
199104.84741303686
157785.5316022428
490564.25883710175
194742.24655291415
191786.32361771745
91000.37394686985
242144.30974585176
110165.19783831722
115734.15582125756
115191.55479136505
113273.8681844129
211526.14223943718
257888.03493959073
142700.88636606093
126877.85129425545
125823.68644795723
210302.56880101754
142547.4926932351
176568.5664515197
152882.00814444738
121707.21695151419
223607.1853257251
150295.5514356801
157872.65194328115
94179.3984390383
171031.54990296916
368521.0619235705
243808.9953803845
210164.39334370792
197752.062190591
296161.7536445439
179418.20341221063
165845.19105197294
220980.47670904317
91693.61397651717
75756.83677336275
167128.95157895432
401885.8956597288
93598.2263589867
111903.93902838267
144380.47903499435
166675.41477077064
142236.1074351601
199556.14066941055
152728.28196951866
224562.47699881066
112844.0249889477
195116.24390063394
69392.56809503472
152910.93051597138
273013.3728481435
113921.39961418985
141680.77505778865
221524.36340046645
106146.0172538931
230617.20892299633
172625.58272171495
147046.53900362275
187838.9953293562
220393.93926681313
90542.02790324634
204883.02481685614
198320.58939521312
160043.48787619043
138009.467742248
210284.74718719648
140567.88559310543
281874.3399034293
324890.5878301775
238723.8020595799
154131.54590186858
321639.8889266253
144917.35840372762
133733.09609842615
129109.11718168386
196794.8231735069
153192.70812424325
141763.34171582924
155868.94411280286
306950.1891557019
249555.1691165953
284106.79818530474
259576.18804268332
138645.51326999237
198420.2719742688
162490.38221622744
120290.29170704974
403505.83447911555
115902.19099801063
310843.9156398703
99005.2825508583
159166.19871221547
185357.9238220019
68568.20615205039
141547.48691033397
198375.01111633377
246584.1772383856
160882.67871222584
262915.5828077017
129289.31017987755
186208.3579266168
323793.952485735
150379.7428984989
338461.11602887843
161995.77197290826
238071.47649035856
154687.84648592488
157929.7967071134
82037.82509494643
205907.98693082482
93875.50383691657
136024.39010857238
177522.28395199875
244751.07572246875
161597.25292652135
245999.5513655138
245039.24965392606
169004.53444684696
198823.22808513942
391029.95702409674
141844.77097458782
175946.1254873918
153279.90148690072
187854.49844963124
163510.66838776827
220066.94966785156
141086.477533727
104024.73876420033
83709.28156728782
158845.17456464565
121783.80948687765
112976.01326035753
101394.47723767819
193101.82402479832
113294.06753504871
199700.1915063781
300393.02811452234
213907.20116373076
196055.7844285147
118151.68707252748
127338.22480096824
170145.30364338055
127678.77515967301
287514.0224983935
197154.72612057088
154279.31623302647
124074.21279235583
251438.52756378744
118361.35690112326
157552.95546553077
91009.6056220856
223331.1937328137
195884.1761506873
136788.99159447406
196227.84981789358
186065.31900901804
156264.09616010118
120507.39336696762
187703.25104633885
133344.46034228464
221411.5837046505
273414.59250400506
138243.28974167613
154168.09967589806
237002.61116090548
193130.33270678407
130098.73656310822
133922.67294745977
291585.92961627035
173238.89172057953
224093.15011155908
208900.51227792577
181337.45082924844
205040.73117555364
181745.097268843
193652.92944206658
200232.22110693782
253277.63878452982
382872.2736062026
205128.0950232365
246370.4036659686
95646.10118143426
415976.46369788965
172937.1849757477
100274.05087843077
154367.68998769863
172577.62907602405
107604.81497118628
346203.18646553566
201846.81189579048
160820.54421833664
188782.43397789556
285812.00686460803
183631.01795020915
510061.9206549179
99132.77935052183
131468.2627014273
139827.73179003331
166717.54229004474
55293.98347272086
160402.06816405882
169733.39017890568
133535.09891227484
215443.2156863576
173163.16613421822
123625.56954627248
165758.52506450075
141262.42058214906
168118.40663993635
198236.8380706108
122822.09598475265
217932.17313006756
305562.4983619129
233482.8903580817
177170.25112964807
181730.48116851106
135828.3248253727
370809.79961363727
133600.12797227886
149297.10414519874
169522.61795335126
115921.16857102736
242750.0923904967
110045.51908050576
222931.2666656207
134931.52149039696
173073.9649192563
237490.7217667202
257433.49600851178
177379.06997183885
183704.53611638094
298921.35332253313
185862.24351243433
157785.63606245565
254832.39294353442
262467.08467880933
284818.55971934245
363762.9404445376
114821.16276081835
193230.88920487213
103435.66426356674
192301.62289017302
224048.22554755097
206997.52661709036
114736.90870273596
293715.6483379167
122750.73652928851
134553.59487882708
122846.37688104197
81629.76018261447
92316.67425079095
309310.7826911304
132451.9572864124
267296.4129697268
219328.76376073496
129289.60442630341
178832.25234036273
191391.88926585446
94213.41592859568
93394.53614172235
191867.22184627148
260312.6188592123
142590.10499889695
92082.72627702483
250765.4887101852
291108.6204797058
273513.17008480715
169114.74842419234
126550.66059385329
188590.9803618697
220653.81555790285
173640.11644220602
184839.47715872104
120975.16466614262
149159.50786661665
168612.75076115725
144523.34337707583
120468.03068500488
160593.5430968936
156600.73649301892
118410.7360690458
178080.68730616212
260377.4539807107
250861.83656789287
192297.3701725937
84203.16513603453
92202.91281284405
224942.88647243532
184110.46625616724
125599.1761922822
336227.5757339665
172000.79888605175
156682.1325947711
168210.5492515646
168092.89347810153
231591.6906725655
220947.35120645273
164065.15538598626
156142.69406414838
108571.71396793169
173015.01189911674
171594.49462412327
277732.148567936
219354.84732545135
191893.83637370143
224662.90652293418
263181.4267577853
111519.54917366791
126952.10418729595
109847.1628173803
125143.21750778727
322611.3845502836
125003.24785445143
77877.78272391175
134858.60352868808
115888.18933988552
108862.81651668328
202920.09280985847
219598.3180713127
140843.9531712991
390010.88949262566
137384.2057693384
184853.17165117757
112282.99373816246
534450.5530465221
130108.20538457303
231395.15786645425
126460.97947586699
269726.4397587038
136473.21244693873
208529.9854425576
131089.7333521358
192623.7894847842
132837.6223246956
206337.33131220881
235641.7949874274
173943.00593178513
115801.12176235465
125342.24531393698
193799.49154929083
106238.40815411392
223294.9652844677
188909.54375188224
118038.13446538738
210298.85311094194
190369.54968515426
207757.72613901366
132716.87044963634
144372.20510944145
155156.42683849478
110911.83291702076
92323.27753259255
190539.8941747586
139812.97199354056
261485.48821248306
224783.5195880662
91215.7866430879
126683.2593936141
186541.5703390922
174371.19154830108
129052.45061092799
207422.04103392523
222839.27942949638
160706.81304427932
170602.36927629166
146794.9999037213
449371.4216975262
133309.94967214757
535246.7765199651
278023.1839565548
79096.69014836638
112110.67377901201
197399.1069549808
103908.0527174012
246384.1290014494
120144.51143533851
149099.41218934514
122939.82754195908
186136.91564584308
229793.64231663698
111557.57169941653
184081.74146973455
103571.72842477758
202887.82245459597
215186.80018281768
131023.5069355103
141048.4288310262
278624.1739725228
188558.66790684083
182876.9430895048
135734.46276793224
139979.96586406388
121170.60642326127
169626.59793259902
86215.94025558319
209835.64450470475
168548.27747848595
197767.2229398421
155304.0542812073
139535.43051914778
151267.96891084342
190591.38772924014
104265.75822110046
160162.46456358107
142918.87141698136
179638.0046912735
212044.7298289591
109489.77728991128
103224.51746102313
243264.17543423703
178391.1788845877
162080.33571452912
115090.76909161349
111972.71430641784
100860.44805244538
185726.20910833622
314245.48205969983
148698.43368578542
129776.73084617428
255375.40542416024
251800.5950398256
221536.68327321898
440705.91635380994
214638.15105386512
153549.52715288533
100975.66130737506
282930.8041925073
245961.7288799237
149855.93700271196
121809.66092512268
65754.14428279603
245563.20519806858
186734.04554434805
67742.36995646232
353171.6381595006
218885.17141364017
199841.5884243994
153564.39480952
182321.43162649413
179561.79458171577
134664.37049165712
253789.86133677655
80403.81301933824
61382.74043274725
221667.691379963
323497.7224227685
194081.83093044788
160676.94087735997
199805.87382752646
275409.54262246005
152456.34054335544
95510.9645264445
149557.93235689812
141217.1197032026
91224.65641010639
127256.55568457494
85414.84863799518
168539.19637126385
162294.23248799916
219248.32538887887
224689.41152913155
144799.2597495669
238064.275725467
151023.68777356835
109725.7494048672
169887.1040598677
317759.6685878254
402324.8075805367
143593.19289347564
297497.43253999023
105368.00159387237
128614.56944672602
248907.04076375542
262648.59325915115
132268.82743783106
265833.7616933238
153979.69398496996
252668.28183164966
124278.03624021138
160463.26531042898
159029.89541422974
62592.08456291589
79884.57695546557
111432.3915053308
132903.74668838654
173288.46230567052
122920.59195528153
140929.2674505048
197285.2645686441
93280.63785152714
117448.63445833986
157171.55629571114
229775.805089908
155288.51646062374
80089.20569079088
100589.01127995717
165559.14591700866
101935.38754879183
224748.3838374709
126080.60283079803
152289.18091809071
223874.11388477572
153039.4318765618
181947.8638451251
112527.48142477113
81435.7936242015
177181.57826546283
143919.87270854352
346845.83761667676
102454.89719202793
147445.75590877683
319046.9535954693
137023.7096844034
131667.07394808138
93082.3617273828
109418.79778839742
376494.6375147316
211139.79371911
125413.25087095359
133274.85651463576
93653.77693313672
149348.3942384414
152650.4693263111
147473.6324126997
132773.37812820097
128322.27312215033
287055.40107683575
197331.16090430747
129350.89281306778
216450.9854764668
293785.7143287247
144971.66898634934
197210.8020557071
189561.87035542502
107769.90084008375
142821.2170145616
79977.44475337648
145224.32168225013
122430.90902210724
116643.35781015774
149546.45335781615
139070.56906196734
57029.76552020144
256328.75792546492
94158.85058155548
202656.58395943386
194225.81419719273
108324.93306539935
344454.4531864561
146506.23382523307
143928.56938373542
197659.97352072995
241652.73066165613
215175.6170362074
127353.24562241047
134682.5668433751
139729.47738048207
221112.2796298336
100411.67972311431
116956.93453148672
237612.30263611907
120333.61232845813
278319.2787268509
282710.92412989435
137162.71793460436
378645.9180064721
127972.47198749357
170626.0808963302
158083.3745476458
136123.1591890432
123848.238950891
198035.34450124227
205924.13947655016
292366.7641864476
170432.521424668
124639.1663367662
229250.91071489418
261378.23567746856
171852.25255526876
86660.18209395347
387486.7476819473
218897.22562533306
226997.64920823305
111827.15113919051
107390.25363742103
189677.393462601
139663.16814262897
225374.7161386943
176029.29344965666
255437.49504699165
214618.8343555706
95735.54294907195
343446.5295298786
88043.22415769985
146519.94277090393
163509.154058946
97594.72914806734
167546.39518890242
160837.75385243076
239082.13682617107
93633.52824078451
130780.01490733886
212281.83439924748
138434.00682474478
140623.98731692156
211807.77836431572
180382.12178808847
138326.78797402355
140466.8898028088
173906.33799987225
197716.79964828165
113603.83519100102
116993.01529774831
137687.97220108146
318594.33702977933
176484.35144309292
129566.93403413665
124647.24435953496
171482.36575619358
186218.22770696747
146722.15334836624
182070.1679567789
177660.90478947997
98474.96336081823
95207.96453889678
101822.79688203816
205700.50794120575
247701.4998259111
257866.81196666392
157831.4229130984
180857.56576319202
140668.4032965731
110451.3579711461
306841.01431493025
128667.04959330351
378339.1655334153
137763.67846848862
173896.0869598955
110000.34067017284
197671.1205183214
245568.61285681662
130761.41820692587
103646.3770056079
136418.48527226216
276830.48882179416
202129.48340145987
317518.0147351404
109338.92228618037
125676.67749095258
168968.37984671493
126235.55144771961
261832.7744535573
159832.3974291407
317099.32852359355
218139.4370188822
173163.7341847096
131167.76718527923
203772.1860672275
203738.09747108296
334839.5447452317
132232.940108945
209752.05073408733
311002.0761173652
191913.4667154914
208258.3915402366
163867.29851604844
162871.45366147495
180638.58444132484
119837.5069251193
168374.12728081507
116535.82472223933
200548.84826583485
217587.84394887846
125416.78659390195
99502.6915525946
102000.08797571836
240382.23249020416
173871.16908205935
153605.79751665567
206872.2678994608
145634.95798896765
158387.1895120634
110435.45596752461
88117.18064227284
181653.08204326904
186196.29461496128
134439.602233577
288174.60443578765
116825.85688303022
211387.36788291522
141559.85868638323
71626.49182299206
148928.24610430113
166631.70859854497
248620.8763867008
413035.6084799015
196562.0977416989
144553.9112061217
149896.73386940235
254093.1188601329
125886.52625765867
215097.30865500745
261955.87774839444
163793.42013172407
107798.63689953148
181861.50233545317
119807.88785863674
154602.07179090075
204583.65029666977
122340.75795869391
174265.01143745292
449791.15608376777
231998.42375991395
103012.38957171809
97365.28381349905
137916.33555703258
121217.44034012231
213015.41109287777
197282.45251564102
145278.34336173758
88579.02134077839
140049.97043057432
175423.23242801792
202806.65114959693
165946.96975167788
157636.9487913459
182689.5999185939
248753.9851437469
118540.19659393215
222381.66150883934
235629.78075445784
237008.75773181103
220640.64649962363
169802.1452924192
130485.48182677
99476.4037538674
90854.58765460826
213134.8143711107
363023.30279903085
155189.63412779657
180854.63137693223
216448.8196349147
133731.26913992467
98007.1782148693
195510.41290336545
187071.11843216256
118889.89153344948
69810.54616740714
97374.57672897904
300177.78134306567
317486.80374847515
247914.8302910967
126462.37334325377
131127.93252188814
228932.86244568118
96445.77776061803
137071.75425731964
113400.32470918074
140291.2529489134
223435.86511062997
200379.52408236795
161211.0498638821
85979.25002239061
94527.64067481781
150147.90922862687
125374.37036577563
164955.6111069762
132939.77812810976
264168.6331365024
164241.67835901317
135294.26451561908
140306.44105331964
264923.4620171985
139176.30033554867
241851.48944777812
141151.49765879902
236951.04633155811
129790.96823440248
80285.73165534993
174823.7601452368
113388.40242093065
109549.0186616094
125857.45006031761
103295.9102584346
159372.26742402857
173997.61100759314
187946.67784673072
181005.4487985337
205777.75623617586
352237.1959319177
218844.30627847704
161805.2451359751
322198.06731389795
214394.44287302013
120550.95912724223
213891.2670694456
219535.55705770437
187000.29092522268
244799.32249059482
135789.60667833907
206854.7718155482
121785.01518622115
130014.50012262243
132370.9139913439
254796.33546784008
243898.43486703487
116505.82103132438
234456.87451545926
254252.27792552224
171755.5224339835
176941.78944714225
236370.88488855158
186755.41577006853
335975.37451901654
143393.67894700836
153856.74131132095
121110.70588011688
129641.11321301528
275653.3049213872
96526.42948659067
118600.59005885597
288651.8600132386
126715.57508499417
189605.68101829823
89722.72842525301
148850.1543895256
261267.1437821498
149972.53417650526
119712.21283507241
160536.72707931531
123782.81270549998
154693.75715471775
168787.13952405294
142371.61467650556
180833.8628611994
186897.7024592575
219907.95778171238
171630.88526531626
143993.13400202684
167156.10880531443
139604.93298111655
119227.94327566889
153049.47968238738
303053.29979658744
114206.55103839227
127161.23707667162
284657.93004509446
195636.81299362297
311547.7283010794
147327.8760237929
247359.363952098
169306.04063954038
133361.9987987427
184644.91134768448
149904.13431901103
161361.6110367095
162534.3092367579
174490.59955869036
138588.33460641894
172893.33290875182
91368.47519964514
134268.2589659183
110364.20472511307
205580.41501879046
94503.82999779389
136175.58401551377
190166.9208806117
158364.76718414348
237132.49581038102
119253.32262099966
328378.42283693014
249409.52378851027
125900.17661718544
118385.199765416
280880.91741943144
83301.7726168551
103993.96472727065
118259.75361983832
135590.37678582539
149352.0940572155
221864.17668121128
207576.12114431238
146720.46049161098
126200.16092193284
169717.4288657894
196422.48132367316
149839.4296217
205444.5787417752
136289.68893953352
331960.778606853
112778.16966716267
106829.70504394919
222725.2946786693
142863.0625747932
129386.38975808106
93016.66529241047
151651.97292369412
215029.87466005416
90288.03764245925
227963.67358171518
147511.54197485448
214039.34326103213
156290.79822933127
173010.01023174945
172024.40184051706
140745.41323725556
313961.4825021935
210206.80695720986
159650.07871025224
169941.43454786227
137711.09965555262
213862.61424811388
142692.21879521883
154309.96135996736
229856.34799263542
129642.50317658542
169247.20900419494
238506.48064571718
195424.59642397362
155763.03991186505
118700.01695855508
164797.70065492106
391366.7297138942
97907.49723568815
205833.6200800778
345252.51972608065
130843.27753688193
125251.41539294587
203591.61925773957
129788.74798964828
107044.31274945299
189657.69567719536
276407.8317884868
155375.99225239342
175654.85742266764
148914.77025401505
210152.15069998652
114569.75042759217
171823.96649315913
143600.00270544586
276643.13822247344
243884.6820329833
227224.7252593994
97908.70916968673
223075.77368235277
204553.0214979989
280049.44303191785
137454.24668925203
145873.88119702996
188237.9826462497
137610.06170822078
327029.76443560125
107137.26317805986
225562.36757803234
159619.56005935397
324374.54062057287
105039.43655230761
140171.77135952737
215223.47196973482
127194.78671350704
269683.301051094
175530.33248828817
201081.1673079819
157706.3239255567
196900.19211652258
246221.04966861458
146349.67683088724
213052.03809390715
175879.2879016242
131786.51070902892
59094.17410068538
124161.84485611365
197689.2648820829
192134.7348651203
157391.83678303089
113380.41549254909
202334.63908880833
207134.8773256
347633.06034019514
280665.0191870263
536649.902599767
109669.08242013655
208380.03483835
179879.39998477208
104208.72725154947
149114.96086541488
158018.09367489026
137011.4779770258
146129.27843046683
172761.44035668575
94349.00469023515
299794.11297981895
184842.05897633792
140700.94763000964
128975.69450352543
180181.96420493518
199668.045764098

# Evaluating on testing dataset
y_test_pred= lasso_model.predict(X_test)
print( 'r2 score on testing dataset:', r2_score(y_test, y_test_pred))
print( 'MSE on testing dataset:', mean_squared_error(y_test, y_test_pred))
print( 'RMSE on testing dataset:', (mean_squared_error(y_test, y_test_pred)**.5))
print( 'MAE on testing dataset:', mean_absolute_error(y_test, y_test_pred))
r2_test.append(r2_score(y_test, y_test_pred))
MAE_test.append(mean_absolute_error(y_test, y_test_pred))
MSE_test.append(mean_squared_error(y_test, y_test_pred))
RMSE_test.append(mean_squared_error(y_test, y_test_pred)**.5)

r2 score on testing dataset: 0.8950229007608679
MSE on testing dataset: 0.017769092377509992
RMSE on testing dataset: 0.1333007591032774
MAE on testing dataset: 0.0923855598882854

for i in y_test_pred:
    print(np.exp(i))

151867.56477957996
313575.0164873651
98220.43719605358
153279.2867311146
317341.2137051542
82917.46092957689
245611.0235329899
140499.3291210323
79891.68132671695
141332.71085340367
144742.1243881481
122123.38821972009
92237.8699148967
218486.62095497892
175442.78224572935
136663.0266465386
198361.18035534982
131001.41202858946
107574.88286030733
211195.8498467439
168109.92268771908
205681.386776068
187140.847913331
136764.6311722077
199343.4868495306
154866.6834451124
197257.79056119782
107643.42857021015
173460.66894816587
201209.26374657382
129922.99765054611
279593.920879489
196497.34698466476
109230.71888534036
273693.0529531759
149856.8502158368
136811.2873302936
207322.95770896223
330761.69580954977
107071.26022973155
139570.77899725782
235348.5569493612
114422.67472688916
316372.6655116767
129784.65215732185
132651.91034275628
111482.00769496107
131772.36068381602
426375.2582842056
120428.3442806038
122236.00779669017
200141.34732800012
107664.95480231773
282536.8638799721
160155.46908042114
219101.168006188
219170.82540326417
159586.04611178115
138578.20193345135
110955.24665260056
72703.18317626341
169547.51630528807
307723.6022366916
248582.71951883571
303511.8874817752
199420.99503847933
105319.51519009602
313599.85947761615
114267.26424829944
167396.0375561145
125740.57622825041
124483.46841044017
110041.89887921071
84571.64536591401
432346.759440454
202251.9201643859
291237.8494324643
316175.0261843128
147439.73658406394
115149.15738173567
115988.19046435445
72549.17290141147
112789.42907792497
103692.2423167436
161534.3205105404
124804.6542213286
262608.3568951328
224873.6121326141
150059.55138402956
199679.52261159176
120195.68061336374
132570.90582159738
135579.1154309972
263345.2937366388
102752.6511363388
184279.1591950292
173158.31672039424
182566.4768010959
195295.93360166484
266213.98588597984
148581.15841946992
212293.38880863806
237732.29301373975
138438.34698307142
177978.3075917538
184219.324600398
163091.1524630311
283372.4213542233
141291.91636232575
197610.22846656025
56549.55739126374
114600.96915236741
144044.5640501339
140189.0433557172
199275.17225748755
112401.77256854835
106260.12840294198
121694.33478655867
127780.134355992
277251.81327301095
132644.09958858628
148161.72148504798
178253.8457883581
189175.4670156891
181114.0546108714
134672.04992654664
230411.69101752678
94472.82341317648
147038.39116372084
199459.09525842543
191983.6213133707
321180.5791869427
188561.37361278737
122664.84451807202
70959.74478253907
372600.2744547634
329129.52234660747
137102.2233879104
219506.72019349123
586319.0376395289
332513.4157817163
131629.4649007834
172416.05684871366
155126.10115419852
123767.06148369783
127608.62333430743
246077.91779738566
185602.1211854127
124146.6178980655
67674.26123694837
119434.90988753176
141238.54354154738
237896.16613418178
148558.81771820356
73379.84238302139
121113.32747346676
119289.15649230286
139461.69629695668
90750.36185368887
135194.61606064983
201496.16051609314
127542.28414829425
292412.3021681539
137458.65808669274
112374.69330349349
115492.79599193239
246310.50820474455
294411.36778966524
436011.73964356456
214369.37668593234
374778.4719929739
88357.29987062425
110397.39729614886
150092.465104742
300525.0082621423
114969.67324027189
121757.44375964873
212526.07113402055
122575.3075476641
158191.49548227072
196146.58241848968
106346.46734693712
124857.71508026622
144453.43727862212
250159.53449359193
124418.91133284006
274527.5395474732
223679.76854059476
203913.41876713102
85660.53078858045
111636.82383230775
98739.18308876372
142078.33929211233
133605.2223148992
196072.068639007
166172.5284010004
216424.05065957477
96576.35340665984
209988.1009047809
140875.95563637966
229843.41211383595
214359.8613370173
112668.04623721712
304634.80680062086
200411.92623837784
123124.3536892239
233041.49005747147
132076.60314962166
138689.47232219912
112542.29875815185
230949.15228242674
159574.09308750235
110738.60706605611
160895.54916199035
215384.58850042138
241209.466196324
206449.86364091406
130811.92942957123
119128.11346550296
133826.32632611683
142196.18048845057
233920.55923129004
190891.29605073793
98559.62737293438
231058.7241371207
138099.7697929118
97589.29525165135
103518.12431980805
163839.1409768653
106446.01446594998
101320.50807101848
175727.9227037424
126529.61867129449
116067.90550323411
233725.82733703434
140860.2619650036
201351.9082532106
158322.05385379776
234161.1838317766
131642.8558354805
109971.84060853969
250206.92931325643
222513.21890179897
483535.3785946369
188374.81351387643
120384.49587357449
146613.26946779928
171282.98902912776
141586.9099687426
103191.2958899197
159819.33963663506
189218.14343489494
141513.67987069293
97441.72468062381
144918.05219823925
144806.8059240995
115664.30102915381
112353.52349507893
174118.36410931667
248063.25369373595
291913.0767895879
179444.16546611174
130088.36655457242
232664.03315404474
314564.2549247673
225022.90091565784
152977.99703826997
141067.4638438407
118946.31194310526
193667.6755648981
406826.7691452497
240390.67010536516
237228.71836625572
92741.54567151306
104109.9943150872
133832.7927033681
134601.06684070357
275281.2621487855
211987.38192677445
130651.81299687397
207217.6467171189
91800.15587004476
194385.44498231402
112982.27756904568
294911.35303515923
178366.09673198982
208006.58447137053
111889.64860917047
256375.50828858235
209466.5329760599
111044.30677889612
120272.13099538928
132744.4549973532
183066.42165081462
85178.38079784816
154052.99797176514
161975.44942670062
140879.33605583978
177399.4102604113
117166.77489207305
181466.67670507598
222300.31851520555
133975.9278009153
134540.94377813645
170907.75822639337
202335.06522667935
149582.58843473071
209736.72081261504
209745.30168641967
118260.99181279777
140078.93474267604
170714.12108555305
98065.20342074468
205167.74537260152
123755.0290374732
176350.86952621
189384.2249656377
170111.57785900435
289339.0404687095
83439.56853368758
214348.48382594038
138284.92098726876
137231.68979644604
83667.13131595613
192351.81231746287
142135.74115665132
136196.33358350472
220477.8010986316
151279.28564493725
102850.39740535154
135907.21226145103
143122.34105363057
148247.00806482194
202196.40895326884
156652.50112378987
125289.42051801668
161549.46304871212
89219.39392079443
81143.83595067906
199279.72071933906
194426.8395551972
145047.2456241951
129727.88846363696
192230.8796755299
233131.75037221765
356068.7254936117
358254.5410358476
106246.37333752755
233590.27514239418
123971.08610153521
244069.42254944396
337677.6711180453
266324.765561922
176106.63116174605
228596.92435774204
135707.3789264614
122438.144291915
86203.74067534032
214712.20282655995
330489.208749718
189803.08402673027
124741.58119488427
231822.13322590885
235214.40453359237
128241.34630904974
189321.94944344953
158806.6333519834
112524.50829500695
123295.92387766084
151369.92287137033
119525.5893421724
179118.57866621338
118913.2123352597
202289.3333921115
178398.61021128905
133880.607380609
222793.67106282568
188780.4098235699
95559.15760094367
208138.23310760516
145259.69588686526
173733.8317354475
195465.51655591422
159489.73178654673
126044.80803230173
233629.2396873605
137582.86830251548
115687.48916364509
264686.89444190473
176614.1025550146
123257.56363697747
138468.33726858275
137875.43682191742
347501.9811946846
138651.10172033077
417797.98863935214
127850.68313410315
197038.9618975747
171237.8710339889
127006.50530212045
169606.22841607756
375917.84613559383
102912.409320999
154278.48272748885
142768.04160742092
101416.8759551442
191262.22766675122
131006.27938638606
219641.34372539536
214607.62061067266
242557.87806805526
214689.26957675844
99519.66569039927
318411.28090736456
150818.09849140138
332593.2561280573
127390.28336821828
353455.91974576813
261097.25978379353
122941.46601965665
215735.11681254362
254138.71809421014
216769.67152177918
175980.34559071588
266171.14088138065
120168.5875986118
228429.21614140156
188382.06564563676
213437.40841552423
226872.1151339634
118806.02163249465
150825.2823454288
161978.07108223526
131582.006259392
122324.17793231644
204882.82108851848
133434.80712041678
410072.7603351436
185563.61820440448

# Checking no. of features in Ridge and Lasso models
lasso_coef= pd.Series(lasso_model.coef_, index= X_train.columns)
selected_features= len(lasso_coef[lasso_coef != 0])
print('Features selected by Lasso:', selected_features)
print('Features present in Ridge:', X_train.shape[1])

Features selected by Lasso: 111
Features present in Ridge: 228

# Lasso intercept
lasso_model.intercept_

11.71667710864116

# Top 25 features with coefficients in Lasso model
top25_features_lasso=  lasso_coef[abs(lasso_coef[lasso_coef != 0]).nlargest(25).index]
top25_features_lasso

GrLivArea               0.327289
OverallQual             0.303682
GarageCars              0.192959
OverallCond             0.149110
Neighborhood_StoneBr    0.134646
YearBuilt               0.109206
Exterior1st_BrkFace     0.101865
Neighborhood_Crawfor    0.098681
Neighborhood_NridgHt    0.097413
MSSubClass_30          -0.095560
TotRmsAbvGrd            0.094190
BldgType_Twnhs         -0.087679
BldgType_TwnhsE        -0.073091
CentralAir              0.070966
LandContour_HLS         0.070444
BsmtQual_Not Present   -0.069620
Neighborhood_NoRidge    0.067973
FullBath                0.067666
YearRemodAdd            0.065814
KitchenAbvGr           -0.060217
BsmtExposure_Gd         0.059748
Neighborhood_Somerst    0.059742
MSSubClass_160         -0.058783
ScreenPorch             0.056547
Neighborhood_ClearCr    0.056043
dtype: float64

# Ploting top 25 features
plt.figure(figsize= (7, 5))
top25_features_lasso.plot.barh(color= (top25_features_lasso > 0).map({True: 'g', False: 'r'}))
plt.show()

#KNN Regressor
uniform  = []
distance = []
r = range (1,21,2)

for k in r:
    
    # Euclidan, 'straight' distance
    model = KNeighborsRegressor(n_neighbors = k, weights='uniform')
    model.fit(X_train.values, y_train.values)
    uniform.append(model.score(X_test.values,y_test.values))

    # Distance is inversely proportional (to lessen the weight of outliers)
    model = KNeighborsRegressor(n_neighbors = k, weights='distance') 
    model.fit(X_train.values, y_train.values)
    distance.append(model.score(X_test.values,y_test.values))

uniform = np.array(uniform)
distance = np.array(distance)

plt.rcParams['figure.figsize'] = [10, 3]
plt.rcParams['figure.dpi'] = 100 # 200 e.g. is really fine, but slower
plt.plot(r,uniform,label='uniform',color='blue')
plt.plot(r,distance,label='distance',color='red')
plt.legend()
plt.gca().set_xticks(r)
plt.show()

y_test_pred= model.predict(X_test)
print( 'r2 score on testing dataset:', r2_score(y_test, y_test_pred))
print( 'MSE on testing dataset:', mean_squared_error(y_test, y_test_pred))
print( 'RMSE on testing dataset:', (mean_squared_error(y_test, y_test_pred)**.5))
print( 'MAE on testing dataset:', mean_absolute_error(y_test, y_test_pred))
r2_test.append(r2_score(y_train, y_train_pred))
MAE_test.append(mean_absolute_error(y_train, y_train_pred))
MSE_test.append(mean_squared_error(y_train, y_train_pred))
RMSE_test.append(mean_squared_error(y_train, y_train_pred)**.5)

r2 score on testing dataset: 0.7593989113518294
MSE on testing dataset: 0.04072567256387984
RMSE on testing dataset: 0.20180602707520864
MAE on testing dataset: 0.13836867552595092

C:\Users\HP\anaconda3\lib\site-packages\sklearn\base.py:413: UserWarning: X has feature names, but KNeighborsRegressor was fitted without feature names
  warnings.warn(

for i in y_test_pred:
    print(np.exp(i))

130263.92352517834
274392.68462010636
108485.03108846341
125486.17853290921
301244.88959496154
94594.92632276706
216357.774785413
154473.3495359899
94341.08676923535
123782.88996087824
136227.24869205547
126857.33831285939
115893.88320380231
205650.84513491546
181919.29059639561
132448.61612708136
195267.66820962538
133168.96849925566
118831.81518609506
208121.30824092345
150185.5111954182
205217.09989656997
205789.18348780726
134192.72422949356
207186.53445732113
196000.88212496066
202779.68865479343
118559.78137715926
185468.32485129035
209529.56966425598
123043.53654160627
253824.59714702718
128811.68955056902
120744.88757976884
234176.18916300667
139250.188540382
129972.68135422784
212310.79349667983
235383.0745493509
104911.27440675284
125387.14662035879
211562.05661863319
122481.96713652078
300134.5583426642
129837.22317936619
114419.79137813482
126456.17637647326
125545.73597697764
350415.09496315056
136341.27456657495
128536.56782383415
197156.13896472045
136422.53783765147
258228.7554054993
174668.6399083254
211215.8986355976
224973.9482042791
180647.20641859504
144480.69776920934
106040.61414428064
107572.62115671708
145186.36395787448
265451.6607077129
231814.79880823326
274730.99413890624
137393.91553598703
121391.724584503
273100.13867916376
109366.57564300479
154343.62081506551
111616.07451667795
135928.95465107364
126559.01433180475
102509.49684971894
385040.465223579
210703.54868915706
263574.74911167345
279712.9300670719
135949.7562370697
120574.00768235086
115417.6392392647
100627.68001635614
125357.83103832473
109943.28235501713
169095.2318825413
123402.97786152469
221238.6572316845
218239.7551278697
173543.7701657609
194505.53467648683
115769.6977273864
160616.77100938922
129238.9349977062
225615.86992102597
109320.66556917729
202727.9170575034
129216.25677594451
179422.84537016822
211852.82460193205
242568.16634692697
152506.95633744728
212674.0863497485
185540.32616815952
115163.71769101132
179057.39906062814
161812.4406082581
137486.5650846205
226154.19778086175
155921.2777623464
213831.2852890683
78288.57558790571
107273.673268038
126863.29997658497
121503.47485913758
193862.542198858
131114.4316347254
116142.56199793598
124219.9849739887
109217.1611211516
221021.9543030447
158282.3466772774
171486.60997007377
185600.8898693412
157996.76022734147
176961.52409546258
129426.67107878074
219865.32711641907
123123.82424246673
179798.48027079744
181631.37785182436
203707.33298299104
301409.40906642587
195143.36741520837
143514.66472347296
115478.04497946028
347137.286385635
333248.1016018266
132338.32378200383
224903.52507463668
325931.98524975224
269681.30617924465
126602.26561209057
212635.79663618043
135765.30178141742
131299.09901785105
136602.9368905479
180448.94247247916
200634.0322971917
134284.33974433786
88138.35175674423
122912.01883831748
156415.57042233975
226873.20042956027
140713.94241959875
114127.58130645099
126310.56429728131
139288.18547465478
138062.68519212233
97042.09827365185
141476.37058298907
200032.8773874487
135838.79098881697
281403.0563083592
134530.85594667238
124645.54353544628
110622.29270674493
261024.699720477
291290.3456640483
337721.28487453103
145474.35530942096
295717.5838702669
97743.8221817952
135085.38833716247
130590.5009282094
265116.2252866836
119418.90324566889
121648.02932882638
218320.70522276493
118356.27542657785
171696.92741231207
184566.8277357928
105498.4545478187
125408.66003839816
130657.43058438327
250987.5293939844
128955.96282364127
275747.0954085227
228342.10465179075
205973.25634844965
102017.56982117942
108616.15860608616
112194.69387104831
136770.13450671968
341498.3009930766
216430.1952544836
131980.62193843868
206930.6422770926
114255.40375680238
183618.4725615771
142829.62774208124
193805.22446788635
192285.3968467048
123683.55253106588
265593.7603470749
204903.69182476157
137569.58350862467
222786.714210492
127857.3567040589
134812.39562182495
133393.89549939518
233501.95364329367
153508.19283640996
103434.99213875124
157831.2613160623
170568.06082371768
224764.01263867208
197809.12289632094
155629.57173973988
141820.05506688982
119581.09047606176
126707.51700282178
226301.66465116554
194455.39922168053
113044.34217687914
204223.2226138595
137083.81955869537
122281.41827377048
109636.89386296106
171416.62862939763
124759.52972334356
111145.06186702364
192300.13545196512
135031.63780509064
130046.86643876732
230559.5920236662
128066.51209261054
209956.30581298808
168680.7078915838
237828.46341032407
152436.38340493708
114809.346511658
227643.80313802336
214137.08637969752
286230.615143868
186464.86068989988
113254.98518970296
168711.12635192962
184328.97563642345
144728.03243703552
112041.18837405436
206246.08747711199
199785.27310423992
131857.1717310082
98454.2133359056
141460.87974278093
147411.93419445402
123090.70563526926
104783.4357518716
199202.61898118086
234586.7107504917
239403.89778723713
163823.90810813464
127939.49853195914
230217.39213789228
346407.27789697
230533.99804069666
170948.18647864097
151167.56078439672
127854.04160617068
188912.7718700841
352506.06529785896
232108.2037243315
224290.07663109718
112717.29756732911
112025.84898475866
129903.49683504016
138034.7406797406
265159.3023311475
164182.95456395132
132204.29007395866
222460.98327812436
101641.25715896147
212763.6036245921
122661.46637373064
272364.0950368569
159389.88980063077
207139.53985126928
111485.21619613493
278719.69212525326
212265.80830971414
129276.951197961
120981.7655296048
130859.1235530913
188800.0044646927
87554.55288089528
138510.36203234276
136268.92524022187
130497.17193978518
195293.69227992467
127196.12406531675
170760.74998638566
233059.79336276648
121213.01409986822
148783.47146365183
161640.03762211953
158187.77991161257
136078.76272545237
206017.5439815568
150662.28254541598
122828.98912025415
152201.42435840252
160999.03726095663
112294.76124580152
141004.35265782292
126506.47057432913
183970.5419769726
207634.91351726017
178698.85595608113
230881.7638555396
103420.74585785609
231120.44308954457
134335.89339532366
105208.19499004429
92607.38485684208
160100.69060323198
152174.55847187754
132782.20402611457
206117.1426109731
232394.35720422477
118785.15265221389
150097.74919638442
140492.7365502465
140612.28480849758
160485.79241892358
169858.51738642537
125492.74344485784
134286.2696325827
96998.91241811163
91598.0030847106
236300.66291986645
190051.4455798341
128127.18421319143
99369.78951027752
147125.1957188004
206028.89847746358
284070.1590438477
333647.22647143726
104654.1961597431
193423.29349479947
136587.80290925148
159529.45267525336
298889.4734593039
263421.1092853393
210738.6511879795
212169.86987734883
147998.48025250487
133197.13113022604
92294.04538939796
223174.04966276162
291237.4460353801
186947.30473733888
154334.28720458102
211418.62922669254
217502.29174856213
128272.98703664086
190654.36106088123
253172.316592014
127715.58371305917
148267.1384550845
140087.4309020075
123288.82466366491
183026.28041444617
122838.19191232411
201450.4868281442
143008.70242122893
123427.10045420816
177385.1115207849
173097.62747422274
107430.52894238813
199279.22419566498
137355.21057217824
146468.03739104306
194067.88188430632
153780.9412304393
126405.05676580207
237626.9910993317
134018.78568337878
109566.57353083862
220932.99624973556
153714.08778021592
123236.29970413001
129409.57165662911
148232.11147779008
319894.1758444601
139235.10151604374
369125.0128932592
117537.33260428612
153511.8043870696
197598.85158033628
149568.39500210993
184826.21469716137
276841.15655641985
89558.72763492927
137812.49777102342
136674.17163595842
108027.99530510849
185710.26122475197
134550.99928150693
173273.33205649914
215632.72471413866
234615.7859512314
229483.14016431491
97267.86630832958
316880.9161809277
130174.07197185245
291785.93897907494
136723.34303654937
299688.92993015354
227896.8686040407
124472.58003317697
226852.53782640444
238088.15584602146
223898.3282561876
177123.82087055544
226173.2718875218
105794.58973542719
241009.30404235885
181022.1138006567
204506.53048892977
226425.88204192338
130060.870054897
198098.4221899425
153868.64023444435
136922.2602633567
122169.05521571085
210198.50606031992
133956.384126458
346655.7750165102
144799.9033679391

#CrossValidation for KNN Regressor
pd.DataFrame({"k" : r, "uniform" : uniform, "distance" : distance})

	k	uniform	distance
0	1	0.688579	0.688579
1	3	0.748051	0.744032
2	5	0.762067	0.756299
3	7	0.768034	0.759591
4	9	0.775751	0.765670
5	11	0.773687	0.764302
6	13	0.771112	0.765221
7	15	0.769263	0.762941
8	17	0.768009	0.761952
9	19	0.764796	0.759399

params = {'n_neighbors':range(1,21,2),'weights':['uniform','distance']}
model = GridSearchCV(KNeighborsRegressor(), params, cv=5)
model.fit(X_train.values,y_train.values)
model.best_params_

{'n_neighbors': 19, 'weights': 'uniform'}

model.score(X_test.values,y_test.values)

0.7647962134922948

y_test_pred= model.predict(X_test)
print(y_test_pred)
print( 'r2 score on testing dataset:', r2_score(y_test, y_test_pred))
print( 'MSE on testing dataset:', mean_squared_error(y_test, y_test_pred))
print( 'RMSE on testing dataset:', (mean_squared_error(y_test, y_test_pred)**.5))
print( 'MAE on testing dataset:', mean_absolute_error(y_test, y_test_pred))
r2_test.append(r2_score(y_test, y_test_pred))
MAE_test.append(mean_absolute_error(y_test, y_test_pred))
MSE_test.append(mean_squared_error(y_test, y_test_pred))
RMSE_test.append(mean_squared_error(y_test, y_test_pred)**.5)

[11.77883721 12.52380267 11.59573492 11.7387911  12.60959677 11.4641086
 12.28779409 11.95436471 11.46164892 11.72647046 11.82152507 11.75387638
 11.66038012 12.2318138  12.11524215 11.79329228 12.19195485 11.79632495
 11.68796014 12.23404335 11.92016228 12.2269022  12.23362148 11.80765431
 12.24036218 12.18885425 12.23444849 11.68815651 12.13341097 12.25264705
 11.71984453 12.43337121 11.86728373 11.70962847 12.35830043 11.84263787
 11.77542781 12.27394906 12.36324441 11.56343405 11.88134058 12.26010234
 11.71836017 12.61260702 11.7729154  11.64838249 11.74637632 11.74034776
 12.76410562 11.8246619  11.76420838 12.19261668 11.83355454 12.46074505
 12.0999207  12.26188974 12.31840703 12.10450898 11.87967321 11.57306255
 11.58502374 11.88201536 12.47759403 12.35260292 12.5238024  11.83276037
 11.70486166 12.51391966 11.60463103 11.93523559 11.61313071 11.81819139
 11.7503997  11.5450739  12.8522425  12.26077875 12.460478   12.53762459
 11.82096623 11.7010879  11.68988423 11.51767003 11.73970883 11.61201025
 12.03957346 11.7204259  12.30319285 12.29208625 12.09608467 12.18490814
 11.66008182 11.98425602 11.77117191 12.32097489 11.60565722 12.2196957
 11.76562627 12.11658944 12.27014187 12.39772391 11.93217474 12.27847606
 12.13544587 11.65458167 12.09734659 11.9921195  11.83296113 12.32862696
 11.96560404 12.27375555 11.28198442 11.5863905  11.74944628 11.70937266
 12.17687264 11.79096895 11.66446811 11.73011456 11.60040497 12.30201106
 11.97371049 12.09455901 12.13402168 11.96841587 12.08476662 11.77095167
 12.29707615 11.70028779 12.10951557 12.10966858 12.22472093 12.60373446
 12.18467907 11.88611487 11.65688282 12.75763733 12.71715448 11.7990454
 12.32778883 12.68380465 12.49070375 11.74574565 12.26816687 11.81812544
 11.78490785 11.82679657 12.10301515 12.21419466 11.80874663 11.38901147
 11.71945196 11.95662738 12.32916322 11.85542648 11.64828207 11.75211814
 11.84336126 11.83642454 11.4831178  11.86249168 12.209885   11.81697383
 12.546411   11.80814653 11.73157467 11.61716821 12.47002014 12.58214174
 12.7239746  11.88429749 12.59278975 11.49390882 11.88891721 11.77844519
 12.48129427 11.69383158 11.71048098 12.29362884 11.67583686 12.05932766
 12.08450661 11.56824128 11.73940518 11.77774048 12.4343365  11.76991955
 12.52568174 12.33983108 12.2322879  11.53703228 11.61351467 11.62761405
 11.82435067 12.08713218 12.28676258 11.79059151 12.24037593 11.64624434
 12.12871525 11.86829603 12.15952806 12.16754826 11.72518083 12.47333336
 12.22685644 11.83456643 12.31667139 11.75965823 11.81061342 11.81830423
 12.35996154 11.93881551 11.54970173 11.95778407 12.03834072 12.32190375
 12.19898522 11.95585051 11.86180458 11.69260515 11.74756904 12.31508782
 12.17897364 11.63410001 12.22544928 11.82593762 11.71341196 11.60548233
 12.04917425 11.73507374 11.62057968 12.1694933  11.81497077 11.78187409
 12.34772567 11.75677367 12.25946017 12.03389789 12.37861893 11.95263075
 11.65203489 12.33496134 12.23972719 12.55514469 12.13575541 11.6354392
 12.03457395 12.12647322 11.88347115 11.62829654 12.23885654 12.21246907
 11.78545707 11.4950315  11.86046895 11.9108396  11.71894516 11.56987811
 12.20574033 12.36174474 12.38612107 12.00451344 11.76160475 12.34736719
 12.75411966 12.34907819 12.05812158 12.09681482 11.76001308 12.15046613
 12.76907395 12.28075235 12.31654235 11.64803622 11.63761758 11.77719228
 12.03502976 12.48726838 12.00505291 11.79313876 12.31682719 11.54589948
 12.27388115 11.71948604 12.51253736 11.98222    12.23651733 11.62118578
 12.52889412 12.26730435 11.77585096 11.69775059 11.77818046 12.15110684
 11.38092813 11.83774754 11.82453616 11.7800391  12.18044081 11.75777763
 12.04797014 12.35417635 11.70518368 11.90980481 11.99504089 11.97034544
 11.81433916 12.23344589 11.92209021 11.71839364 11.932442   11.98587172
 11.62957316 11.85673981 11.74412321 12.12962461 12.24114152 12.10431204
 12.35026385 11.55728102 12.3506217  11.80931656 11.83268521 11.43754781
 11.98408649 11.93220633 11.79552068 12.23094789 12.19677863 11.68331209
 11.91743862 11.85293185 11.85523513 11.98699226 12.0472613  11.74129456
 11.80487121 11.48261351 11.43378078 12.37429821 12.16129283 11.75879827
 11.50443904 11.89752657 12.24371212 12.55523195 12.71983559 11.55364116
 12.17263214 11.82373819 11.97651457 12.605104   12.48158891 12.25848676
 12.27088563 11.89653565 11.79912581 11.4351004  12.3156165  12.58287128
 12.13174905 11.95520063 12.26894022 12.28899469 11.76273515 12.1670265
 12.15060783 11.75970929 11.90841029 11.84931221 11.72267225 12.12110928
 11.71876402 12.21872422 11.87165515 11.72197995 12.08085271 12.06704289
 11.61142896 12.20342347 11.83040429 11.89417746 12.17960678 11.94212853
 11.74729049 12.37129543 11.80692718 11.60664706 12.296679   11.94310357
 11.72118749 11.77114409 11.91568436 12.6672096  11.84339774 12.81772376
 11.67239444 11.94270361 12.19301332 11.8827181  12.12819394 12.5254037
 11.3959717  11.83406718 11.82504521 11.59809595 12.13282796 11.80783244
 12.06061346 12.27923353 12.35779542 12.27164621 11.48999429 12.66486746
 11.77565232 12.57685023 11.8266953  12.60358867 12.33741663 11.72861133
 12.33074767 12.37743877 12.31895091 12.08513977 12.32783572 11.5668028
 12.40381963 12.10782325 12.22429818 12.33060008 11.7748934  12.20221569
 11.94489831 11.83022704 11.71419963 12.24991822 11.8035087  12.74124129
 11.88389249]
r2 score on testing dataset: 0.7647962134922948
MSE on testing dataset: 0.03981209083016479
RMSE on testing dataset: 0.19952967405918548
MAE on testing dataset: 0.13763353158806296

C:\Users\HP\anaconda3\lib\site-packages\sklearn\base.py:413: UserWarning: X has feature names, but KNeighborsRegressor was fitted without feature names
  warnings.warn(

for i in y_test_pred:
    print(np.exp(i))

130461.99189524478
274801.0515804788
108633.47961795505
125340.7244145375
299418.2703952052
95235.55236407349
217030.6991051755
155494.35511651236
95001.59135082894
123805.92053715135
136151.7113431041
127245.8579901885
115888.07391671668
205215.06461661603
182634.49654913446
132361.52443604424
197196.2793204567
132763.54251470132
119128.75193532005
205673.11170915308
150265.99187841732
204209.6009948536
205586.36283271605
134276.22061882238
206976.8388950135
196585.79964629211
205756.45439292878
119152.1475019512
185983.07868405269
209535.20492508865
122988.30287165196
251040.91408239026
142526.57163813585
121738.24297846718
232885.0886371412
139056.81487156634
130017.95201186753
214046.60800475103
234039.31838384652
105180.58934998074
144544.19446886805
211103.1881278937
122805.87956947068
300320.95221265015
129691.70271858636
114505.99915032407
126295.07609424581
125535.98922983759
349446.45392327965
136579.4670152506
128567.37689174459
197326.83191510173
137799.43471647918
258007.78918258174
179857.59931252323
211480.85231197343
223777.388399024
180684.73258600887
144303.38539315012
106198.21161820255
107476.09618231362
144641.7624703967
262391.7861499468
231561.99752093886
274800.97732061933
137690.04330335508
121159.32068541445
272098.5677417299
109604.2056604745
152548.15390023513
110539.77763418734
135698.58157303973
126804.23265144248
103267.07828436732
381643.61349598237
211246.02865923283
257938.897283289
278625.7029358188
136075.64651755829
120702.95688027846
119358.18743412005
100475.58370565111
125455.8053335609
110415.9908751301
169324.70107076064
123059.82552623062
220398.5660066339
217964.23104661138
179168.9819043446
195811.57727784995
115853.50938901697
160212.44841246813
129465.78446720961
224352.75507362263
109716.73835326341
202743.25394936942
128749.80028816656
182880.72407752532
213233.2405497615
242249.6082928465
152081.9416174114
215017.79209747983
186361.91978869893
115218.04628102922
179395.22114055988
161477.24322314156
137717.68804934694
226076.1034044187
157251.86591651334
214005.1911336681
79378.62632563422
107623.09038835272
126683.39234761178
121707.10475788872
194244.43862554594
132054.36149658385
116362.79179162055
124257.9039651398
109141.98930211247
220138.25402701556
158531.80097777274
178895.8393991538
186096.6938920923
157694.65417637356
177152.57046551644
129437.27415978814
219054.56819909456
120606.41952614706
181591.61405442833
181619.4013718838
203764.64896647434
297668.120504082
195766.72952575792
145235.9394791658
115483.48602548124
347193.42652934184
333418.7492128595
133125.21047165053
225886.70142897512
322482.6620697927
265854.3151232488
126215.45052257663
212812.51927414557
135689.63213974098
131256.3877472481
136871.33082715448
180415.0207660504
201631.01766893922
134422.9740636416
88345.58223881912
122940.03045093498
155846.58607779891
226197.37002928718
140846.5786403863
114494.50102577545
127022.3252232977
139157.44387129176
138195.4883233726
97063.22049701968
141845.21182912868
200763.9259262754
135533.46001452138
281084.6086051189
134342.33111195065
124439.46686740631
110986.98363324218
260411.9669180649
291309.5545130819
335700.4745342391
144972.23066955505
294427.9942913468
98116.3032606719
145643.51118965997
130410.85729574216
263364.4959271475
119830.26698799006
121842.07086746096
218300.71939481757
117693.24064703975
172702.83190335124
177106.5157265364
105687.43411275886
125417.71725396205
130318.98866843166
251283.35904458628
129303.74776925355
275317.90950116474
228623.3296618944
205312.37942109688
102439.97296041514
110582.22789222353
112152.41261624712
136536.96647980256
177572.13171141996
216806.94505066762
132004.5293300997
206979.6854578481
114261.42955492201
185111.80024489775
142670.92509891343
190904.4010536921
192441.64868650108
123646.35892854932
261276.19844021893
204200.25640492808
137938.94362031514
223389.32649048694
127983.70525640494
134674.14839491976
135713.89418665358
233272.25838882395
153095.24259316534
103746.08765529201
156026.95608669132
169116.09719680026
224561.24446566802
198587.5269182018
155725.56080273478
141747.78326302092
119683.39321199579
126445.80093490424
223035.8543683148
194652.97557941853
112882.19207052993
203913.11643875553
136753.8158851982
122199.71103636367
109697.55172717189
170958.18052343288
124875.65180177466
111366.25904670682
192816.31812622768
135262.25112532606
130858.79093965773
230435.35990494696
127615.05984295016
210967.66783551182
168366.40853352746
237665.36428984176
155224.968420353
114924.98449311411
227512.70074525033
206845.45233466444
283550.2659809343
186419.61559494067
113033.46415033565
168480.2733574134
184697.23895552524
144852.48348522492
112228.9810506952
206665.44151201707
201283.38449299868
131328.49650287713
98226.51818737447
141558.5864417929
148871.6198104518
122877.74030076215
105860.56815586484
199933.54797463331
233688.60030499016
239455.068133756
163491.03625845723
128233.07030258469
230352.76948747836
345974.2610278894
230747.2388360742
172494.66434282335
179299.84935929015
128029.12787383953
189182.25239021433
351186.9385296801
215507.79214664715
223360.5039980246
114466.35551769238
113279.96279454867
130247.56659000626
168557.0862513664
264942.57452047366
163579.25847714816
132341.20565059138
223424.1336826459
103352.3677909761
214032.071522958
122944.22008899302
271722.70441027096
159886.5852346351
206182.5718317116
111433.77853765422
276203.7565940367
212629.0436062781
130072.98048565128
120300.8048806448
130376.33816193254
189303.5028715021
87634.33268551224
138378.44089634306
136562.29434489246
130618.88678818711
194938.77331604497
127743.24437373357
170752.45214713106
231926.63061789982
121198.34248297183
148717.64784231302
161949.67126547347
157999.22952859948
135176.84519203633
205550.2675354458
150555.97380002652
122809.98943391746
152122.5922496574
160471.51371226305
112372.34686518689
141031.67803653015
126010.83921805756
185280.2098027953
207138.2083768456
180649.1520336859
231020.98975480234
104535.39739273408
231103.67485743738
134499.60782115793
137679.69356064213
92739.31885319416
160185.28976183137
152086.7452112377
132656.8078258993
205037.44216388182
198149.8078797412
118576.32099785634
149857.27476164047
140495.65638847032
140819.62976536236
160651.42942553255
170631.4598810088
125654.90239649732
133903.036870187
97014.28488634621
92390.62397729441
236640.6930175784
191241.6006777956
127873.69089557335
99154.94816285306
146902.82098645924
207671.36269391578
283575.01052865025
334313.8810408764
104155.59475982173
193422.48850992543
136453.36550216415
158976.9609867309
298076.07060310926
263442.1048270633
210762.40968061105
213391.89326821396
146757.32452395838
133135.9160530679
92512.62514575478
223153.80003744556
291522.15346451435
185674.24659355154
155624.3901339035
212977.16182036992
217291.42157813435
128378.10688259252
192341.26664451207
189209.06132996464
127990.23958311348
148510.4026158786
139988.0313758759
123336.5716407154
183709.18747724392
122855.48489422111
202546.3887621474
143150.9790330081
123251.21524159914
176460.5667595379
174040.42719721366
110351.8266604316
199470.8662699163
137366.01514350105
146411.65046779072
194776.25786034114
153603.2919111171
126410.58361609081
235931.1800288782
134178.6205924088
109825.39438566526
218967.58819307544
153753.13457750742
123153.58184525899
129462.1822081244
149594.61700618782
317175.20527530345
139162.51993151344
368694.5346413813
117288.78774893099
153691.65160429888
197405.11640324024
144743.44414797187
185015.32562850878
275241.3684412604
88962.63267724651
137870.09493628665
136631.82962648832
108890.26954579467
185874.6799598967
134300.1414169616
172925.03521340204
215180.72276599912
232767.50952158848
213554.25703331942
97732.97466781779
316433.2062184272
130047.1448355144
289772.1589715875
136857.46971774046
297624.72904579033
228071.99663421605
124071.25619462677
226556.05344084615
237385.046334967
223899.12814344768
177218.68734667022
225897.2938169763
105535.51434417034
243730.8039599567
181284.56364548553
203678.52671270288
226522.61818807686
129948.4876948296
199230.09553663337
154029.32856344254
137341.67034977317
122296.00144878101
208964.1988070504
133720.71709774015
341547.242067101
144913.52898096296

model = DecisionTreeRegressor()

model.fit(X_train.values,y_train.values)

DecisionTreeRegressor()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

y_test_pred= model.predict(X_test)
print( 'r2 score on testing dataset:', r2_score(y_test, y_test_pred))
print( 'MSE on testing dataset:', mean_squared_error(y_test, y_test_pred))
print( 'RMSE on testing dataset:', (mean_squared_error(y_test, y_test_pred)**.5))
print( 'MAE on testing dataset:', mean_absolute_error(y_test, y_test_pred))
r2_test.append(r2_score(y_test, y_test_pred))
MAE_test.append(mean_absolute_error(y_test, y_test_pred))
MSE_test.append(mean_squared_error(y_test, y_test_pred))
RMSE_test.append(mean_squared_error(y_test, y_test_pred)**.5)

r2 score on testing dataset: 0.7752183153170734
MSE on testing dataset: 0.03804797950079332
RMSE on testing dataset: 0.19505891289759952
MAE on testing dataset: 0.14360439892102086

C:\Users\HP\anaconda3\lib\site-packages\sklearn\base.py:413: UserWarning: X has feature names, but DecisionTreeRegressor was fitted without feature names
  warnings.warn(

for i in y_test_pred:
    print(np.exp(i))

149899.99999999994
275000.00000000006
159999.9999999999
144999.99999999994
378499.99999999977
79000.00000000006
201800.00000000003
148499.99999999997
80999.99999999994
108000.0
189950.00000000012
108000.0
86999.99999999997
159894.99999999994
192999.99999999997
131999.99999999994
202500.00000000003
120499.99999999994
108000.0
204000.00000000006
179999.99999999997
173999.99999999997
148499.99999999997
117000.0
188499.99999999997
161999.99999999988
250579.99999999983
86999.99999999997
186499.99999999985
176000.00000000003
117000.0
233000.00000000006
177000.00000000006
104899.99999999994
275000.00000000006
135999.99999999988
159434.00000000015
194999.99999999994
319000.0000000001
165000.00000000006
134899.9999999999
233000.00000000006
114500.0000000001
317999.99999999994
132499.99999999994
138999.99999999994
114500.0000000001
128500.00000000012
415297.9999999998
138999.99999999994
109500.00000000007
191999.99999999994
100000.00000000001
234999.99999999994
139999.9999999999
171000.00000000003
225000.00000000015
132249.99999999994
165000.00000000006
90349.99999999997
60000.00000000002
156999.9999999999
248899.99999999997
236499.99999999983
275000.00000000006
234999.99999999994
114500.0000000001
305999.9999999999
146999.9999999999
142500.0
128000.00000000004
114500.0000000001
109999.99999999991
92899.99999999993
412499.99999999977
144999.99999999994
336999.9999999999
327999.99999999977
132499.99999999994
136500.0000000001
94499.99999999999
91500.00000000001
129899.99999999996
117000.0
163990.00000000003
107000.00000000003
252000.00000000017
197000.00000000012
139999.9999999999
200141.00000000006
142953.00000000006
106000.00000000004
108000.0
293999.9999999999
117999.99999999991
144999.99999999994
122000.0000000001
142999.9999999999
221499.99999999994
191999.99999999994
172999.99999999985
223500.0
327999.99999999977
171899.99999999988
217999.9999999999
142999.9999999999
143749.99999999997
319899.99999999977
137999.99999999988
197499.99999999997
54999.99999999995
147999.9999999999
120499.99999999994
159434.00000000015
237000.00000000003
88000.00000000001
79000.00000000006
111250.00000000004
167999.99999999994
312500.00000000006
165400.00000000006
142999.9999999999
175000.00000000003
240000.00000000015
180999.99999999985
123000.00000000003
268000.0000000001
127499.99999999991
124499.99999999993
176499.9999999999
196000.00000000006
319000.0000000001
225000.00000000015
126999.99999999996
60000.00000000002
378499.99999999977
582933.0
117499.99999999996
213000.00000000003
625000.0000000002
289999.9999999999
144999.99999999994
183200.00000000003
175000.00000000003
129899.99999999996
120000.00000000006
222500.00000000012
180499.99999999988
136500.0000000001
61000.000000000044
104899.99999999994
179900.0
210000.0000000001
131999.99999999994
100000.00000000001
137000.00000000012
109999.99999999991
155000.0
88000.00000000001
128500.00000000012
170000.0
146999.9999999999
372401.9999999998
119000.0000000001
115000.00000000009
124999.99999999991
142999.9999999999
295493.00000000023
345000.00000000006
222500.00000000012
326000.00000000023
100000.00000000001
159434.00000000015
168499.9999999999
301500.00000000023
159999.9999999999
135000.00000000012
165400.00000000006
109999.99999999991
168499.9999999999
217999.9999999999
109500.00000000007
142953.00000000006
171899.99999999988
277499.9999999999
139999.9999999999
305999.9999999999
173999.99999999997
202500.00000000003
83499.99999999993
106000.00000000004
109900.00000000003
120000.00000000006
124499.99999999993
180999.99999999985
177499.99999999997
187500.00000000006
106250.00000000009
265900.0000000002
116000.00000000006
302000.00000000023
260000.00000000015
165000.00000000006
312500.00000000006
196000.00000000006
128000.00000000004
226999.9999999998
132499.99999999994
124000.0000000001
120000.00000000006
214000.0000000001
165000.00000000006
76499.99999999996
131500.0000000001
260000.00000000015
237000.00000000003
159894.99999999994
132499.99999999994
112500.00000000006
144999.99999999994
121500.00000000004
202500.00000000003
170000.0
88000.00000000001
226999.9999999998
190000.00000000012
102000.00000000003
96999.99999999997
210000.0000000001
100000.00000000001
109999.99999999991
177999.99999999994
124000.0000000001
141000.0
236499.99999999983
137000.00000000012
198899.99999999997
131500.0000000001
274299.99999999977
119000.0000000001
75999.99999999999
266500.00000000006
200000.00000000006
466499.9999999999
187500.00000000006
109500.00000000007
147999.9999999999
143749.99999999997
134000.00000000006
88000.00000000001
228949.99999999997
175899.99999999988
115000.00000000009
88000.00000000001
138799.99999999997
139999.9999999999
117000.0
117999.99999999991
155000.0
275000.00000000006
372499.99999999994
158999.99999999997
139999.9999999999
260000.00000000015
248327.99999999988
180999.99999999985
109999.99999999991
139999.9999999999
197499.99999999997
178739.9999999999
314999.99999999994
213500.00000000017
237000.00000000003
100000.00000000001
109999.99999999991
142599.99999999994
130000.00000000006
265999.99999999994
178999.99999999994
139999.9999999999
200000.00000000006
91500.00000000001
186499.99999999985
86999.99999999997
312500.00000000006
175000.00000000003
237000.00000000003
155000.0
200999.99999999994
164990.00000000012
106250.00000000009
111250.00000000004
178999.99999999994
167999.99999999994
121000.0
159434.00000000015
137449.99999999988
124999.99999999991
207499.99999999985
100000.00000000001
182900.00000000012
232000.00000000015
109999.99999999991
167499.9999999999
159894.99999999994
226000.00000000003
175499.99999999988
216499.99999999988
210000.0000000001
137999.99999999988
147999.9999999999
215000.00000000006
144999.99999999994
139999.9999999999
140200.00000000006
173900.00000000012
196000.00000000006
171899.99999999988
289000.0
92899.99999999993
279500.0000000002
142999.9999999999
144999.99999999994
109500.00000000007
187000.00000000017
175000.00000000003
134800.00000000006
202500.00000000003
165000.00000000006
109999.99999999991
135959.99999999988
178999.99999999994
120499.99999999994
166999.99999999985
171899.99999999988
132499.99999999994
144000.00000000012
106250.00000000009
121000.0
250579.99999999983
187000.00000000017
137000.00000000012
159434.00000000015
210000.0000000001
199900.0
378499.99999999977
384999.9999999997
108000.0
187500.00000000006
118500.0
314999.99999999994
412499.99999999977
234999.99999999994
176499.9999999999
244999.99999999983
139999.9999999999
126000.00000000007
99900.0
208500.00000000012
439999.9999999997
211000.00000000017
109999.99999999991
249999.99999999983
210000.0000000001
135999.99999999988
180999.99999999985
149299.9999999999
132999.99999999997
147999.9999999999
124999.99999999991
139999.9999999999
175000.00000000003
144000.00000000012
184099.99999999997
182900.00000000012
162900.0000000001
185000.00000000006
193499.99999999997
89499.99999999997
193499.99999999997
151999.99999999997
159999.9999999999
177999.99999999994
121500.00000000004
139999.9999999999
184099.99999999997
143500.00000000012
112000.00000000003
255899.99999999977
210000.0000000001
109500.00000000007
256000.00000000012
143250.00000000012
474999.99999999994
104899.99999999994
426000.00000000006
112500.00000000006
139400.00000000012
109999.99999999991
105000.00000000004
148499.99999999997
372401.9999999998
138887.00000000012
131500.0000000001
131999.99999999994
100000.00000000001
200000.00000000006
112000.00000000003
206900.00000000003
250579.99999999983
214900.00000000006
187500.00000000006
88000.00000000001
305999.9999999999
156999.9999999999
339749.99999999977
106500.0
426000.00000000006
269999.99999999977
136500.0000000001
266500.00000000006
277999.9999999999
250579.99999999983
165150.0000000001
214000.0000000001
126999.99999999996
222000.00000000012
179540.00000000003
161999.99999999988
274724.99999999994
106500.0
147999.9999999999
217999.9999999999
139400.00000000012
141000.0
176485.0000000001
110500.00000000003
412499.99999999977
151999.99999999997

reg = GradientBoostingRegressor(random_state=0)
reg.fit(X_train, y_train)
GradientBoostingRegressor(random_state=0)

GradientBoostingRegressor(random_state=0)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

y_test_pred= reg.predict(X_test)
print( 'r2 score on testing dataset:', r2_score(y_test, y_test_pred))
print( 'MSE on testing dataset:', mean_squared_error(y_test, y_test_pred))
print( 'RMSE on testing dataset:', (mean_squared_error(y_test, y_test_pred)**.5))
print( 'MAE on testing dataset:', mean_absolute_error(y_test, y_test_pred))
r2_test.append(r2_score(y_test, y_test_pred))
MAE_test.append(mean_absolute_error(y_test, y_test_pred))
MSE_test.append(mean_squared_error(y_test, y_test_pred))
RMSE_test.append(mean_squared_error(y_test, y_test_pred)**.5)

r2 score on testing dataset: 0.8979173082348175
MSE on testing dataset: 0.017279166535059256
RMSE on testing dataset: 0.1314502435717
MAE on testing dataset: 0.09077315700353661

for i in y_test_pred:
    print(np.exp(i))

140708.13233772956
332470.0234738757
119095.99883857284
150886.60087869386
320943.2143227177
79725.6818471504
230081.1632802578
146062.05665816224
76501.12947635211
134897.70921464183
158914.28916320996
123204.28607416539
102345.0185426666
199880.92019352462
171270.3958673714
136908.0301079267
195901.87341200822
128390.70568760534
112236.79941110632
219237.94547420027
161396.80663702288
228896.42863140503
172489.42364707068
122701.07742739658
198606.94345181173
168597.94718265053
196481.91877250536
110596.48329832595
170013.97031362716
182858.46749268772
124591.77217988427
259175.17626281272
206421.74030454818
115862.11811705833
261465.36561882353
146906.72082998467
120506.11448039726
209872.50683533776
329088.425615985
107491.86153697174
130732.16148707645
246504.4308834956
121857.94410196415
345694.32843441004
127534.62697882424
112537.35453826727
117361.04029053677
130881.70602387986
422607.55082352436
135128.81723145585
122253.59267717565
191919.8896294768
108962.46966812473
300787.0948473435
143702.46370124185
249793.6118178364
194847.68463217583
149000.7712109692
145508.4068407708
105125.99490875182
65004.451194742884
147451.45918855668
300819.18274974683
293896.17549450934
277034.93536500016
226050.5201701328
111753.19596918792
335069.8749086868
121268.10150742829
161929.12666269462
125837.09279871883
128973.68766671204
116006.06619786122
91278.46216265207
352863.7502082334
174286.7886177079
321892.1957774616
317640.11748855247
137686.1398596438
117508.52336561425
108335.45533826681
86431.10595164617
119779.70205823325
98929.33275220628
153528.85550105298
128658.25126281113
271431.1260561865
202901.63305486392
143405.22092178406
179000.8069714832
144216.6379957549
142480.31942719704
128360.08955387819
264362.5892771139
100118.7760265502
166269.25308305625
189720.00134031306
170002.90669627857
209990.95983680332
235622.59900131327
168682.6106936469
204695.80540238885
279855.25300946686
147758.16000086543
174521.34169797375
138365.19664267977
152089.4529560399
254659.35884069838
141042.09115727345
174361.0528732442
51825.29199148055
120518.82171540664
136257.71769607914
135919.2922337874
203430.38273808802
115147.785064821
109384.88942160773
111051.95978086202
129319.74600061082
277128.16340834554
153761.14549323192
143405.22092178406
172111.48286808448
195214.66249442886
178161.44737523727
127132.60205577739
238238.34946804564
112080.17577715068
143629.64609867203
184070.96460300498
192050.04666891103
354790.7618644437
196151.80526393314
109190.33611986363
63616.098689059654
350521.3822055389
395617.3540127147
128605.0973725017
235772.4189549346
562621.8612518581
355324.3695403825
130601.64869254085
165989.26173629382
163230.07714578067
128636.73177872307
130791.74379508613
248637.11563840337
193954.79005285408
117929.95143841609
59925.57229175122
112667.12886232787
142902.3908122345
206515.46094542905
139790.2660741526
87971.54616753539
116262.62381724516
127170.76832255616
145360.40862857888
91379.40561395731
130844.3472954855
203161.55139807236
155321.38527663113
320648.6571257984
157557.77354291372
116294.64731482785
107034.69925689072
214745.26611168394
339028.29175430915
417814.97576625354
235864.37627277878
376200.46494836407
93235.99571551288
120097.96713161303
146210.71657419862
333997.184883162
143405.52489842652
136991.49280001686
218552.38131217964
118623.00750239432
172406.83800885934
171078.51342266236
95989.07033573999
129025.25122539226
140850.9529759802
269914.5188533308
142945.71344951543
304944.6285526498
226059.63596759908
184532.8811571981
82424.5002209505
115857.7354725355
108616.11193403054
127398.38209185503
158070.63603148723
167106.47916217687
161644.71375908764
215620.64513848952
91387.98451594393
215634.3183194984
126009.61667246973
215122.4025582446
188449.91225918848
125142.92619383315
349725.6490411109
191219.6551914497
124264.69883307547
252872.41076035425
143380.79114405895
151434.6856258815
115636.0517849128
256746.66471663222
140999.02035959857
112346.23209157497
163833.0852886614
209409.7824897041
279205.2580429309
184108.4608982958
142050.03718622166
117913.73263379264
132369.04003471043
135351.79340198037
206383.11001336342
208970.34981666695
96984.12700846289
237124.4189708159
143405.04646156172
104963.65988657955
104105.16317598532
173467.39146841594
106107.24184943057
107567.67546720884
182655.3721352089
124610.09150395764
124834.12293262043
237423.6227315565
137022.35596797153
197591.44740617712
149678.66789088256
243796.2010056532
122484.31852836718
118265.82566722367
262453.951014605
201447.25578613929
385375.1093196659
181748.7378778706
118611.39549052047
152505.82190161519
165328.89801602898
142001.50536215125
98123.2528840535
167703.05812590383
173892.9638313459
139481.101463981
87174.17155177308
140913.34302589906
148079.66975007963
112270.82494730805
109344.61051379555
175337.3235429706
266776.898453688
281865.14775100065
162578.2126125384
127860.55881465868
234551.55182034068
285971.77299701096
188559.70283647132
175231.72284587656
141190.83830731447
126273.54526346842
170486.76934300005
396169.5176241579
200024.06699696777
227629.52190170594
95678.26308747882
105470.34317755635
133659.29709506256
148883.13626009805
247408.7594202505
211870.24308682763
149657.34397478824
201641.3960427369
92019.31636571368
192313.32420349366
111677.96439843981
319609.97821898805
166531.02027760437
209361.73021605797
119059.26077269773
240196.46460110226
183890.48009770978
124043.41125986128
119463.84112589074
139382.12869289643
176739.0247251848
101051.04619589474
145430.35903593642
134765.04051859764
135228.16851483582
173728.15780271616
105170.61654883533
179968.93183465648
238017.812113703
117732.78003426283
147183.57281001259
179976.61542224622
213935.0888402398
156113.23655039544
194771.1525369557
206927.41667821773
134524.0981323788
159276.80787480905
192144.90596606865
90374.67820648848
153270.8822707766
130742.39846502023
175924.24400522045
190730.0111846883
160848.28464473723
305544.2881688981
91744.09582436699
227148.9857232289
138639.40884242544
131960.55752752518
93185.09881116876
182904.30715092993
163741.20559749904
132285.56643925255
186536.87239842344
173597.9493108586
101779.13525528264
152915.59089992166
148928.9760580744
132915.8717476992
211688.66655730305
155932.83761048244
132243.66077580347
119201.89068753034
98494.66505949634
81344.83692743372
202838.79220498592
178955.48623285137
129722.4459387574
138168.53936010879
184410.65394855963
214664.359285564
340197.49966478185
331018.5889092181
122762.99961767161
204852.49124845662
122080.46173703251
316728.2270728153
372608.5067795688
251121.35088899438
160761.33801981685
271580.78228310763
141447.65172686044
126729.27037468662
88598.9663430247
201667.77730439007
361182.7814312383
176272.27736552354
129182.2340299067
259689.5442925935
217050.8409388138
129662.63331619467
194237.28088779873
185591.5731356239
104084.76621660296
130253.07194049393
149939.79343834112
119988.38861988165
171973.28533331348
122665.7899009083
186933.53074910527
152942.99834818122
127763.60611729044
201916.77743885733
195562.8828200801
97077.84320042226
208681.48743070965
149490.43753523126
189447.00695711595
187531.66144121703
163033.19752031128
134784.8757671696
230037.76893879558
140186.2900479613
109190.44657384066
290776.51632234757
179059.4336465179
117996.27106811272
153452.3243560171
137712.57443169213
327945.0462323989
152388.3227316876
423889.4134957632
126572.37908407439
162226.3728396428
164433.9827269322
134821.3895453943
160378.49789962504
413214.0387677283
118802.43215211836
155743.69773125672
138591.90289050806
110854.87435893212
205134.91904661816
139643.07410787622
235929.30417860288
209886.06129323205
217081.2055854618
210822.75918490544
74851.01646677255
286823.9379829387
143317.52206910084
352911.1411196107
124797.97885567194
363928.0806078404
284541.3665469354
127807.96918868122
222606.6922277184
254796.67187934744
209886.06129323205
178728.80163786418
254373.04435403374
123389.92618059838
210088.59689398104
167587.71133464418
188239.64769065566
225824.71736088567
123360.63805927477
148035.299553689
171430.59944024181
138956.76803476617
116316.7303531084
201455.78446404822
135432.7588850054
428509.35492316476
180100.9034882829

from sklearn.ensemble import RandomForestRegressor
regr = RandomForestRegressor(max_depth=2, random_state=0)
regr.fit(X_train, y_train)

RandomForestRegressor(max_depth=2, random_state=0)

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y_test_pred= regr.predict(X_test)
print( 'r2 score on testing dataset:', r2_score(y_test, y_test_pred))
print( 'MSE on testing dataset:', mean_squared_error(y_test, y_test_pred))
print( 'RMSE on testing dataset:', (mean_squared_error(y_test, y_test_pred)**.5))
print( 'MAE on testing dataset:', mean_absolute_error(y_test, y_test_pred))
r2_test.append(r2_score(y_test, y_test_pred))
MAE_test.append(mean_absolute_error(y_test, y_test_pred))
MSE_test.append(mean_squared_error(y_test, y_test_pred))
RMSE_test.append(mean_squared_error(y_test, y_test_pred)**.5)

r2 score on testing dataset: 0.682602215775255
MSE on testing dataset: 0.05372477034690355
RMSE on testing dataset: 0.23178604433162828
MAE on testing dataset: 0.16892784466381583

for i in y_test_pred:
    print(np.exp(i))

128347.2157313713
275025.7840522569
135182.39927129564
152329.46419571684
287878.4878786661
109751.04496100584
204620.12719874937
151772.18334219104
109751.04496100584
137070.7814729995
203925.31900262166
125279.43735329676
119351.24824868738
203925.31900262166
152784.3612357372
125279.43735329676
203925.31900262166
127609.70757527355
128380.65329795305
205594.69650408288
151032.14433157534
270849.11956944776
151772.18334219104
127287.29442234572
207777.1269690089
140524.93062498592
203925.31900262166
119351.24824868738
152784.3612357372
203925.31900262166
141726.5717557559
219441.5161864101
138470.1295791796
110340.03542474248
284016.29682665004
131159.11841908644
139213.2440147963
204620.12719874937
288396.0074987039
127287.29442234572
140106.53456354802
205388.61981027442
125279.43735329676
292473.92074627965
135915.38792178867
140409.0431449171
125279.43735329676
127287.29442234572
296451.12266136473
141444.41859544234
124906.43291248578
152784.3612357372
132729.5764106234
281197.8992260162
137492.2260267253
210208.64261211827
203925.31900262166
138443.4929953244
140049.66120389526
128033.01978465356
109530.60090317688
131544.1242001907
286054.304066583
267255.121780921
290390.17664769845
152784.3612357372
125333.8120358036
286054.304066583
124610.70099102044
151772.18334219104
151032.14433157534
130094.9059815607
112108.45827854153
119236.09601722367
296451.12266136473
143370.8289825761
288396.0074987039
221237.91268979543
129252.02794503437
127287.29442234572
126732.88124662083
128524.88096365561
135510.71621875066
125279.43735329676
149920.43220039873
122612.25465810028
284983.9885867225
205594.69650408288
137492.2260267253
203925.31900262166
151032.14433157534
151032.14433157534
125279.43735329676
205388.61981027442
121424.62278603952
144206.547221187
207777.1269690089
203925.31900262166
207777.1269690089
205388.61981027442
152784.3612357372
203925.31900262166
211717.03047692258
203925.31900262166
149597.53034988642
151772.18334219104
137962.66862645478
274018.73636395537
135915.38792178867
151032.14433157534
108945.93116683864
125037.0682397183
125708.09274643663
139213.2440147963
203925.31900262166
125279.43735329676
110340.03542474248
119707.66340341592
152784.3612357372
286341.3166833783
203925.31900262166
137492.2260267253
152784.3612357372
151894.47225765017
149920.43220039873
125279.43735329676
205388.61981027442
127287.29442234572
129654.91195076672
203925.31900262166
203925.31900262166
293764.88265408616
203925.31900262166
139659.54558388767
111286.0509271136
294339.0428021172
294339.0428021172
127287.29442234572
220653.01351930402
296451.12266136473
286054.304066583
125279.43735329676
152784.3612357372
152329.46419571684
136636.93878154526
125279.43735329676
205388.61981027442
203925.31900262166
128033.01978465356
108945.93116683864
112438.0660181824
144661.81363184212
152784.3612357372
151032.14433157534
110925.81124191586
138470.1295791796
124825.87149602857
145436.71772277754
109751.04496100584
127609.70757527355
265828.072201697
139243.6139828479
287633.5586783002
141444.41859544234
130069.41452116633
137864.81366363764
204620.12719874937
286054.304066583
294044.0137092918
205388.61981027442
295341.905910923
109751.04496100584
138057.85144045323
152784.3612357372
273744.0752166026
133021.767242015
140409.0431449171
203925.31900262166
133820.91150629197
152784.3612357372
141726.5717557559
135379.97304187753
135915.38792178867
203925.31900262166
267776.87399090023
203925.31900262166
290039.67189832084
215589.56677101663
203925.31900262166
111745.55641095081
143772.35559369926
128908.6910704398
130246.91903990819
131159.11841908644
143370.8289825761
139213.2440147963
268004.1992523287
114879.43884361023
265828.072201697
131006.13712169636
203925.31900262166
203925.31900262166
134285.4083245264
288396.0074987039
203925.31900262166
127287.29442234572
266733.793591419
138443.4929953244
140828.3425789166
125279.43735329676
219441.5161864101
143370.8289825761
113468.71687425637
151772.18334219104
203925.31900262166
273066.35001788463
213839.0334234306
130414.50823748426
132521.5642698341
151032.14433157534
135915.38792178867
265828.072201697
151032.14433157534
127229.7650354461
278751.1540717218
130798.2624889135
121571.25071377953
110340.03542474248
152784.3612357372
114240.11406736201
112108.45827854153
152784.3612357372
126732.88124662083
114077.65861321762
266733.793591419
140403.27946088486
203925.31900262166
137492.2260267253
207069.97621390488
126732.88124662083
137492.2260267253
209997.94115524186
203925.31900262166
282258.54302498116
152784.3612357372
131108.5762753764
140858.32951887453
140049.66120389526
137646.68997672608
124906.43291248578
150152.4611003175
203925.31900262166
139213.2440147963
124906.43291248578
130094.9059815607
139243.6139828479
131025.79046915511
130635.87161881877
152784.3612357372
269927.3089629734
291379.5852988488
140524.93062498592
127287.29442234572
206522.53361761256
224591.32980639866
152784.3612357372
144206.547221187
137492.2260267253
119351.24824868738
203925.31900262166
296451.12266136473
265828.072201697
208485.05892906178
129654.91195076672
123762.6026360569
135915.38792178867
203925.31900262166
274183.86699495575
141330.54292708702
138798.75334231017
203925.31900262166
124610.70099102044
203925.31900262166
125279.43735329676
292767.374486162
152784.3612357372
204620.12719874937
121934.29539717831
205594.69650408288
203925.31900262166
123445.80051355826
119707.66340341592
131823.90067206955
151894.47225765017
124239.68763221065
138798.75334231017
141023.28486928047
135915.38792178867
152784.3612357372
112773.83653826754
151333.10341388546
265828.072201697
126732.88124662083
137646.68997672608
203925.31900262166
204620.12719874937
203925.31900262166
152784.3612357372
152329.46419571684
135915.38792178867
151333.10341388546
152784.3612357372
124360.41008413104
151032.14433157534
138798.75334231017
152784.3612357372
203925.31900262166
203925.31900262166
210924.85915893115
119236.09601722367
203925.31900262166
129523.11308068194
130246.91903990819
123696.57945072567
152784.3612357372
133021.767242015
135915.38792178867
203925.31900262166
152329.46419571684
110340.03542474248
140519.34244846206
137498.03686223167
125279.43735329676
219661.69285531077
203925.31900262166
137646.68997672608
140898.58648121016
121149.80024093195
127459.18110555364
203925.31900262166
152784.3612357372
135915.38792178867
138470.1295791796
152784.3612357372
214567.61932831627
286513.8732133899
294339.0428021172
135915.38792178867
152784.3612357372
112108.45827854153
274257.0811010922
294044.0137092918
269213.68688443396
143370.8289825761
289023.6751651717
128363.91697536428
126891.76744782446
124065.97094139936
203925.31900262166
294868.17657211755
152784.3612357372
129889.61421658132
267735.5667426005
204620.12719874937
129654.91195076672
203925.31900262166
143370.8289825761
131757.99374699633
137890.89460908674
141695.8071354518
128042.76104590362
152784.3612357372
143344.2907154136
265828.072201697
144206.547221187
138031.2941638191
151442.2247549178
152784.3612357372
126732.88124662083
152784.3612357372
139243.6139828479
133021.767242015
152784.3612357372
138443.4929953244
138798.75334231017
266733.793591419
133507.18592700938
149496.07640032453
267735.5667426005
152784.3612357372
129142.60914843531
150152.4611003175
137492.2260267253
291949.0834101169
139213.2440147963
289944.2089273275
136597.66458026972
203925.31900262166
140524.93062498592
146818.25946120426
151772.18334219104
289697.52219582925
137454.33778016848
139243.6139828479
127609.70757527355
109751.04496100584
203925.31900262166
151032.14433157534
152784.3612357372
203925.31900262166
204620.12719874937
203925.31900262166
109424.27443889153
292767.374486162
132729.5764106234
294868.17657211755
125279.43735329676
288714.5742249639
286341.3166833783
127722.81988753776
205388.61981027442
265828.072201697
203925.31900262166
152784.3612357372
219441.5161864101
137864.81366363764
203925.31900262166
203925.31900262166
203925.31900262166
205388.61981027442
127287.29442234572
137646.68997672608
151032.14433157534
128908.6910704398
112773.83653826754
203925.31900262166
140524.93062498592
294044.0137092918
204845.6223109089

from sklearn.ensemble import AdaBoostRegressor
regr = AdaBoostRegressor(random_state=0, n_estimators=100)
regr.fit(X_train, y_train)
AdaBoostRegressor(n_estimators=100, random_state=0)

AdaBoostRegressor(n_estimators=100, random_state=0)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

y_test_pred= regr.predict(X_test)
print( 'r2 score on testing dataset:', r2_score(y_test, y_test_pred))
print( 'MSE on testing dataset:', mean_squared_error(y_test, y_test_pred))
print( 'RMSE on testing dataset:', (mean_squared_error(y_test, y_test_pred)**.5))
print( 'MAE on testing dataset:', mean_absolute_error(y_test, y_test_pred))
r2_test.append(r2_score(y_test, y_test_pred))
MAE_test.append(mean_absolute_error(y_test, y_test_pred))
MSE_test.append(mean_squared_error(y_test, y_test_pred))
RMSE_test.append(mean_squared_error(y_test, y_test_pred)**.5)

r2 score on testing dataset: 0.8354327501263884
MSE on testing dataset: 0.027855700781517896
RMSE on testing dataset: 0.16690027196358279
MAE on testing dataset: 0.1262328706994991

for i in y_test_pred:
    print(np.exp(i))

124284.36361622355
290947.70965465566
99086.36681323925
151653.13218656415
314401.3043291481
81930.2660914172
195532.44874503763
147322.22063230647
81930.2660914172
99406.03514595622
150666.8324646799
111153.07660328
99259.8702585835
209325.32293770497
165329.80029875177
124284.36361622355
198693.06222303765
122446.34998583047
111153.07660328
240291.59081230924
159236.91456840065
209325.32293770497
169506.8372779379
97038.65252027851
216579.95085040512
167521.71061453532
175105.57050521235
97523.48357074827
165329.80029875177
172823.5219479795
112439.99966382052
243644.9295823406
170106.51831754556
92919.2210232873
255758.14652910447
135077.59282033987
123971.00856038106
209325.32293770497
294201.7990538983
99086.36681323925
119929.6065050812
226521.72135095013
109933.84535245837
285594.796203534
126316.96036699307
132157.99825003874
97523.48357074827
109933.84535245837
380282.1263961287
124892.2645603842
99086.36681323925
188964.1225038802
108980.46321215664
282559.36367302603
164118.39825837014
244382.09425868173
207585.164738001
134077.4222159309
128430.16295256124
105592.59424122075
70443.83082785219
123971.00856038106
270925.749974753
277570.5940314579
262934.8734465334
200979.0582041887
95674.84360156948
284842.80329290044
111153.07660328
162916.7011530049
131552.54679791752
121061.24057514605
94180.05668126472
99409.06815181115
366075.521266941
173018.52239338556
287093.0262248117
273097.27860032336
119929.6065050812
99358.9178392296
121061.24057514605
86901.74189775104
106825.31890824775
95386.52712561103
128919.36242028303
124213.97814998108
233769.86015114872
230951.5164360896
162405.70681611993
192421.46626826617
152757.66266902894
128919.36242028303
114317.03920926267
232930.94950929907
126107.22836584826
172678.06295775637
165836.0864200435
190968.57844218568
208785.40125018446
226521.72135095013
194515.01498506655
205396.3264729432
251867.2657937112
125697.17590673546
172278.62423498044
165836.0864200435
140572.60269431368
262747.963378442
145203.09862165636
154901.4320264489
70443.83082785219
121249.21258222745
119929.6065050812
122446.34998583047
200849.93364428054
100832.36921445173
92050.22403085661
105430.83467818535
150666.8324646799
271946.11053860077
151542.24044217975
162405.70681611993
165836.0864200435
175105.57050521235
158813.96716437192
116707.42559917689
226521.72135095013
92831.60153023167
127568.02217690056
168865.29064239148
200849.93364428054
292531.69427190034
195388.30934801276
137401.98458128105
77848.74580841283
315674.24012033833
294201.7990538983
109933.84535245837
239265.61663679604
469437.65651427885
329684.403112633
108980.46321215664
170112.63006447197
151653.13218656415
122446.34998583047
108787.24993796686
215052.50489160302
202482.03989947267
106068.33612645257
75037.17521973555
102681.85865112036
127866.66061848743
221954.1361579948
132198.15256736253
94180.05668126472
119929.6065050812
131552.54679791752
125697.17590673546
82678.96391732409
128430.16295256124
172823.5219479795
137919.71841914052
285446.41799465986
162916.7011530049
105592.59424122075
124213.97814998108
213583.17226530332
266124.95309785753
416046.27261441865
185828.52825949652
329684.403112633
82411.24279154248
107875.70611585805
162916.7011530049
273097.27860032336
131552.54679791752
132157.99825003874
215052.50489160302
99086.36681323925
169506.8372779379
137716.70064123097
99406.03514595622
123692.44312938725
119771.7670920638
254440.94610690753
152757.66266902894
282559.36367302603
245346.7636968172
172678.06295775637
98633.83395003254
123131.14925523692
96737.11412474293
111491.4572021157
127866.66061848743
172304.4624172707
149697.5106507594
197269.73463995315
91012.41645493484
210154.94474571422
97523.48357074827
234401.92478956917
195532.44874503763
109933.84535245837
293636.6947788285
173018.52239338556
121061.24057514605
234721.98604344437
127568.02217690056
144472.91919150713
102959.23298582736
251580.64237286663
135077.59282033987
121249.21258222745
158055.78345898484
155759.76406898812
273006.97357422364
173809.45560292195
126514.10153895596
99406.03514595622
129080.8651733634
124284.36361622355
185441.4124190557
188681.98554453178
89430.77273228427
254121.93087828282
121639.24439306781
95386.52712561103
95304.00875848692
162916.7011530049
99086.36681323925
96842.37730004155
172278.62423498044
117692.72405119152
120268.65123681357
234401.92478956917
125320.41758816135
175105.57050521235
167521.71061453532
240629.07049163827
127866.66061848743
125829.87485318683
234401.92478956917
197269.73463995315
322465.0364380993
170729.63048848807
117900.19550208379
151542.24044217975
167521.71061453532
134005.54738841025
91597.07981996398
155008.7214503306
192421.46626826617
124284.36361622355
80962.14282358914
125828.0906620236
138487.7256856678
116257.93000354586
103044.40513894468
170729.63048848807
259137.010502594
285446.41799465986
151266.87615963927
109933.84535245837
263845.4534862652
263845.4534862652
205343.76490273292
155008.7214503306
162334.0309794941
109933.84535245837
176009.70402501497
361143.42008747184
176987.00074607666
218770.48467252922
103044.40513894468
123684.47370679505
126326.49474216287
176618.59109144183
271946.11053860077
186300.49870015265
141609.48915326735
200979.0582041887
91095.379005887
197269.73463995315
94180.05668126472
282559.36367302603
186285.7305285068
224363.02882902595
119771.7670920638
270925.749974753
205396.3264729432
120268.65123681357
121249.21258222745
119079.68562613051
170729.63048848807
87916.38683949155
154235.40069452283
132198.15256736253
124196.67017973654
170729.63048848807
108980.46321215664
186300.49870015265
239265.61663679604
126326.49474216287
131552.54679791752
197799.72900371754
195214.77672149267
126326.49474216287
200979.0582041887
222442.97778029667
108787.24993796686
140607.7880478697
208600.40374515124
87916.38683949155
134376.1556820032
121061.24057514605
172678.06295775637
172823.5219479795
167967.38266596335
245346.7636968172
102681.85865112036
217501.0660510018
120268.65123681357
119771.7670920638
78437.6863549197
188964.1225038802
172278.62423498044
124284.36361622355
176009.70402501497
174094.3084326118
99259.8702585835
133721.21844313823
126126.76472700648
111491.4572021157
167521.71061453532
167521.71061453532
120268.65123681357
103044.40513894468
108980.46321215664
98801.37855130657
216579.95085040512
170112.63006447197
114317.03920926267
122446.34998583047
177805.95539955006
226148.80859715783
283491.30132695264
301258.222945188
97462.45079075286
200979.0582041887
118776.48035446838
283491.30132695264
371845.57413814095
275523.5318436075
167521.71061453532
234721.98604344437
121249.21258222745
124196.67017973654
87275.11940152623
205396.3264729432
288933.78892889357
174094.3084326118
129792.93387755321
234721.98604344437
223657.81328879076
116707.42559917689
213583.17226530332
154901.4320264489
106825.31890824775
126107.22836584826
144114.90063980443
112439.99966382052
164227.62096980188
114317.03920926267
176618.59109144183
136443.3845112394
121249.21258222745
186714.09351480592
195214.77672149267
108787.24993796686
203459.2294677347
126326.49474216287
171250.29504704167
172278.62423498044
135161.36798463695
124892.2645603842
225594.02041577073
124213.97814998108
120483.17483259633
256285.10661444577
168036.02612799482
105592.59424122075
154235.40069452283
135077.59282033987
301258.222945188
134376.1556820032
387133.61257616227
119771.7670920638
180631.74569015283
165122.56651583847
123684.47370679505
162405.70681611993
378252.65400852304
120268.65123681357
139434.84313476473
128919.36242028303
106068.33612645257
192057.72754738145
138377.996353592
208785.40125018446
180968.46354646797
226148.80859715783
218066.58923296063
66702.65669395783
271946.11053860077
121061.24057514605
283567.96708271484
98896.01792653067
294201.7990538983
290947.70965465566
126107.22836584826
217501.0660510018
246077.9600093942
180968.46354646797
164227.62096980188
244382.09425868173
127632.09593887294
176009.70402501497
175105.57050521235
172678.06295775637
226148.80859715783
111153.07660328
135696.53854008907
170106.51831754556
124196.67017973654
104140.74708382452
205343.76490273292
138377.996353592
423642.2034903086
187246.50894349872

from catboost import CatBoostRegressor
cb_reg_1 = CatBoostRegressor( random_seed=13, verbose=200)
cb_reg_1.fit(X_train, y_train)

Learning rate set to 0.041078
0:	learn: 0.3839109	total: 176ms	remaining: 2m 55s
200:	learn: 0.0917148	total: 3.33s	remaining: 13.2s
400:	learn: 0.0660797	total: 7.9s	remaining: 11.8s
600:	learn: 0.0512503	total: 11.8s	remaining: 7.81s
800:	learn: 0.0394802	total: 15.1s	remaining: 3.75s
999:	learn: 0.0314580	total: 17.9s	remaining: 0us

<catboost.core.CatBoostRegressor at 0x2d673a79060>

y_test_pred= cb_reg_1.predict(X_test)
print( 'r2 score on testing dataset:', r2_score(y_test, y_test_pred))
print( 'MSE on testing dataset:', mean_squared_error(y_test, y_test_pred))
print( 'RMSE on testing dataset:', (mean_squared_error(y_test, y_test_pred)**.5))
print( 'MAE on testing dataset:', mean_absolute_error(y_test, y_test_pred))
r2_test.append(r2_score(y_test, y_test_pred))
MAE_test.append(mean_absolute_error(y_test, y_test_pred))
MSE_test.append(mean_squared_error(y_test, y_test_pred))
RMSE_test.append(mean_squared_error(y_test, y_test_pred)**.5)

r2 score on testing dataset: 0.910935258668458
MSE on testing dataset: 0.015075665338152718
RMSE on testing dataset: 0.12278300101460592
MAE on testing dataset: 0.08067298395188066

for i in y_test_pred:
    print(np.exp(i))

145536.5925795407
331208.6618823944
111495.4125997253
151084.82605588835
342387.6057437851
75971.64255242128
229916.72426701296
141405.43422092384
76475.35070315203
134464.92076933675
149608.86159066486
119489.37184898795
106973.59343264543
200288.25085418165
175957.0778921774
134535.76626566163
195808.25526384867
136093.2783709987
114810.76316862379
214071.25622861943
159983.4215276739
221590.50133633584
171000.85838439074
125921.25447811215
203310.56718853352
162533.18468329817
196694.52669191282
103344.27613832914
173224.3469640263
193475.6570046085
127412.41851523638
263655.3466367899
182948.1289481549
113316.0227092939
261792.52595635882
150234.6986472512
130506.18000860902
202156.69076084785
329561.21975316934
115692.99207727997
132465.46327665407
235433.39731340742
118042.61858292307
349776.7685613655
126657.79592354628
127702.47733565154
116044.98260895633
127057.04836076275
417061.1935362713
134469.1783319008
120903.76606625521
199455.80092578675
110411.86595634441
319656.88189148606
148838.58903237787
243736.22115625278
197799.56755635972
151893.86347723147
136486.29125662506
108288.3765618684
68402.98585330248
162077.79733182193
303060.05494240986
284238.9078950783
291496.79600039835
214906.24514958385
112387.2862762651
324107.82716394344
108936.68099580277
163757.5127918995
125150.80639949614
124252.68275649336
112437.67705439386
84655.9996993055
478874.99190664775
185875.03802954193
314833.1373081232
317769.2763643611
142682.41416181097
117967.7566534251
107681.73455370773
69476.18368625511
118032.88318825007
102688.5302816786
154796.0768508523
130206.62326613606
270431.46916823153
210974.21196573635
140879.50188866624
179606.15649354408
142018.53108267806
145652.44163156647
131176.91646709305
265865.70339923183
105217.63655544737
169958.51061449558
168932.53145485886
169000.77696339544
207776.31681588478
249369.91381035754
165720.97334999708
206897.79199295104
246798.02337506876
141287.7673353944
184321.7899603988
153273.89121828703
152061.2140475169
271086.2094659759
145911.44647819272
176962.18932407425
57037.494679116535
110629.40297189585
136215.9410289983
135132.40913392123
201114.96921568835
117681.32484791096
104839.14605999862
109790.91453664354
124648.89317771484
281097.94926272397
143574.22183480943
142542.16299636473
174587.96763696242
206426.40312599938
183686.6895340356
132658.58918578853
230581.4496118231
111143.63974102792
144727.9068528985
183930.55760926084
186247.4846769694
364433.7599942842
192786.14536801365
112918.45215866725
65582.47946227874
352847.45025483717
388611.8720763874
130934.90551458026
239231.83930336768
484795.7387308299
360573.2530364891
134919.69656834565
171400.98221738578
152614.31194632244
134579.01727463826
131821.02045492013
244788.7578305834
189072.85042941166
124576.69754704583
57338.98576884395
110152.32666015008
147833.28338949315
199225.83280027838
154463.9872292436
86320.26581603984
123513.18433834221
131331.04133623993
144410.4932175223
87078.52409388332
136535.3241092869
192386.01948200076
147884.53813873162
337716.5119432198
139617.42718314801
116470.4150997194
113097.2313954969
219937.09278817743
321859.3001852256
424157.62247926276
231738.5141599473
348060.05198192736
82513.60883472298
100451.01271358962
155795.2997649729
298729.7777180168
127265.40361001863
130552.9895561325
218451.91481251357
124060.04623967435
169432.76220162562
184064.35017208496
96227.5964112766
125090.19313895043
143242.46600784737
262368.0869215741
143611.12751465425
308787.027318569
227398.47689869447
191909.12072116154
86306.96234905752
117236.52832520049
107023.86527870523
141116.82291935995
169061.63154510324
172962.68062023947
165042.14843564518
213646.38442422572
94078.315091583
211073.89779052534
123180.84375306833
229683.67914958534
199520.7659484812
124585.54570678652
326779.4739941102
193264.6380312242
124841.37266061889
252445.00403539673
137709.79246270328
148318.06470864048
113587.94993452747
225421.1653213864
137875.72084905504
106800.85602309763
164418.07502246567
211003.11386732382
276110.73167634767
180975.01618394826
148626.3991561709
123887.56063231542
131989.08941924956
142124.56791005033
213778.26066210362
219400.61679049765
95125.33564912969
214342.2840592098
148771.97489086605
96227.63096448449
104154.98629343866
163196.32175833898
104891.51857985578
104703.88871275811
176824.30117935422
120664.99442245865
127691.98967883115
230589.67677719303
142645.0851371207
200945.2819492841
152654.00561448073
252381.46875912335
124574.14001523581
107535.75230330015
235261.7721813042
207421.19533087028
406760.80259004585
183457.51311486994
126749.23032715016
158495.98554586098
176836.36092148925
140090.7299223903
98984.97129498188
177417.96748840963
170624.4844102903
141184.83967369667
82409.44403162875
143720.2627024783
141941.3507275585
117485.45941125756
117831.79046241501
172953.68433109586
264963.1986821306
289560.57761446404
164458.09097098833
132047.06141247333
242414.3412478341
310012.71188113175
208611.57171903877
176225.89437285042
138918.92926259153
120453.70374362738
170519.58437862594
381627.71698586806
207832.26689500382
237825.95554505484
90570.11313693826
95050.95296781072
138593.1772617261
136016.9094951596
275045.24196814134
209629.63145770092
139560.34695177918
200331.11371126122
86129.80037953035
191058.08728889548
109745.57336793232
325959.08837483235
176361.88661961927
217579.85438489838
126084.47204953429
242799.84098763447
184319.21882034992
113253.18534456854
118892.66863344643
138687.07051608837
181061.871378745
93042.60854889391
152462.13627778893
155644.14521115724
134901.1775193055
175599.43558261075
110902.12245990321
181703.46120183074
232426.68099288418
130087.0262854889
145526.4742915302
166261.22144541488
205112.63886425647
148785.41770553874
204174.38481396995
197245.02895452743
130774.98753006948
149066.4744946961
192290.36385273404
86838.85464819372
170625.82388855933
139996.29616128688
168728.7493829243
187755.80080882955
166018.99618147756
302274.31774717686
92720.56438358189
233693.06087174575
137167.94724473497
132202.2155443083
81245.1505435061
201636.0381415327
159055.94795600243
132500.92885903007
199866.72581119527
153675.5880751207
105343.82732956616
154563.62508018524
152683.10313112388
134246.3047378255
198842.26141169318
165489.05545716413
123152.84130059947
131113.81142522267
93395.76897642556
74528.04901375732
217114.34974870397
186549.62519053006
139330.2864264903
130784.76453304074
176178.99358979295
229968.12515639962
340938.55890678853
359942.23210148804
110112.12081589023
234353.59289616405
124352.12814556056
289672.7592554807
375625.99118962244
289345.51184955385
174829.66877874357
253932.1439679101
142611.43263756364
128220.74080762315
85986.02701781986
212145.23418242467
354580.9025455274
193950.1538519174
139167.56726101672
259356.14754898782
225588.57947548258
133232.15346672927
188615.48944982153
190163.02794213942
107855.30013774312
127372.1873647017
149587.6550989608
124183.39940751695
176706.75140677812
120240.09571413753
186989.7470715744
158230.21091427488
131167.33943835704
227363.03064233653
178930.41124694585
96547.34595189671
216324.5715153015
144029.2859419545
185908.92846184561
187131.3365867163
164616.20718170406
135517.523701403
217510.75658906923
143891.27035962872
109859.41129217812
274395.39358338993
182549.31938827396
119238.51080153615
140657.34233636584
140486.2084739426
327522.88018812233
143997.8413265069
443345.5720728042
133722.01516996117
173673.46620208706
170132.7684650762
133126.54434687053
160611.8237024678
386806.52736267156
117344.0334088499
151886.20673293993
148471.3324944289
99302.3631806411
202923.6049893851
138582.60862042784
225667.6161064137
202572.57743859387
212905.54430421168
207343.23561190302
79415.0474730091
315683.95417242515
151190.1894178075
331934.517280458
124647.44953310242
355798.28495013487
282750.04860052397
125060.31409494304
216732.38789589537
247925.0831369289
202618.9783842293
172537.8010068895
253812.5471773737
114356.0640735794
225784.23617235135
168280.91029742634
198687.6333844726
237103.16094892085
119916.29569793842
148696.11250876696
181175.5639203985
134156.67097726092
120072.85850046604
203244.9517853045
132708.31678154974
463212.03806300554
188086.74925607376

df.to_csv('train1')

from sklearn.linear_model import PassiveAggressiveRegressor
regr = PassiveAggressiveRegressor(max_iter=100, random_state=0,tol=1e-3)
regr.fit(X_train, y_train)
PassiveAggressiveRegressor(max_iter=100, random_state=0)

PassiveAggressiveRegressor(max_iter=100, random_state=0)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

y_test_pred= regr.predict(X_test)
print( 'r2 score on testing dataset:', r2_score(y_test, y_test_pred))
print( 'MSE on testing dataset:', mean_squared_error(y_test, y_test_pred))
print( 'RMSE on testing dataset:', (mean_squared_error(y_test, y_test_pred)**.5))
print( 'MAE on testing dataset:', mean_absolute_error(y_test, y_test_pred))
r2_test.append(r2_score(y_test, y_test_pred))
MAE_test.append(mean_absolute_error(y_test, y_test_pred))
MSE_test.append(mean_squared_error(y_test, y_test_pred))
RMSE_test.append(mean_squared_error(y_test, y_test_pred)**.5)

r2 score on testing dataset: 0.5396063748255786
MSE on testing dataset: 0.07792915707363604
RMSE on testing dataset: 0.27915794288115114
MAE on testing dataset: 0.16529953679979653

for i in y_test_pred:
      print(np.exp(i))

print(len(r2_test))

print(r2_test)

plt.figure(figsize=(10,6))
plt.title('Comparision chart for R2 Score')
plt.xticks(rotation=90)
sns.barplot(x=models,y=r2_test)

<Axes: title={'center': 'Comparision chart for R2 Score'}>

plt.figure(figsize=(10,6))
plt.title('Comparision chart for Mean Absolute Error')
plt.xticks(rotation=90)
sns.barplot(x=models,y=MAE_test)

<Axes: title={'center': 'Comparision chart for Mean Absolute Error'}>

plt.figure(figsize=(10,6))
plt.title('Comparision chart for Mean Squared Error')
plt.xticks(rotation=90)
sns.barplot(x=models,y=MSE_test)

<Axes: title={'center': 'Comparision chart for Mean Squared Error'}>

plt.figure(figsize=(10,6))
plt.title('Comparision chart for Root Mean Squared Error')
plt.xticks(rotation=90)
sns.barplot(x=models,y=RMSE_test)

<Axes: title={'center': 'Comparision chart for Root Mean Squared Error'}>